---
_id: '46117'
abstract:
- lang: eng
text: "Let $X=X_1\\times X_2$ be a product of two rank one symmetric spaces of\r\nnon-compact
type and $\\Gamma$ a torsion-free discrete subgroup in $G_1\\times\r\nG_2$. We
show that the spectrum of $\\Gamma \\backslash X$ is related to the\r\nasymptotic
growth of $\\Gamma$ in the two direction defined by the two factors.\r\nWe obtain
that $L^2(\\Gamma \\backslash G)$ is tempered for large class of\r\n$\\Gamma$."
author:
- first_name: Tobias
full_name: Weich, Tobias
last_name: Weich
- first_name: Lasse L.
full_name: Wolf, Lasse L.
last_name: Wolf
citation:
ama: 'Weich T, Wolf LL. Temperedness of locally symmetric spaces: The product case.
arXiv:230409573. Published online 2023.'
apa: 'Weich, T., & Wolf, L. L. (2023). Temperedness of locally symmetric spaces:
The product case. In arXiv:2304.09573.'
bibtex: '@article{Weich_Wolf_2023, title={Temperedness of locally symmetric spaces:
The product case}, journal={arXiv:2304.09573}, author={Weich, Tobias and Wolf,
Lasse L.}, year={2023} }'
chicago: 'Weich, Tobias, and Lasse L. Wolf. “Temperedness of Locally Symmetric Spaces:
The Product Case.” ArXiv:2304.09573, 2023.'
ieee: 'T. Weich and L. L. Wolf, “Temperedness of locally symmetric spaces: The product
case,” arXiv:2304.09573. 2023.'
mla: 'Weich, Tobias, and Lasse L. Wolf. “Temperedness of Locally Symmetric Spaces:
The Product Case.” ArXiv:2304.09573, 2023.'
short: T. Weich, L.L. Wolf, ArXiv:2304.09573 (2023).
date_created: 2023-07-24T07:52:23Z
date_updated: 2023-07-24T07:53:29Z
department:
- _id: '10'
external_id:
arxiv:
- '2304.09573'
language:
- iso: eng
publication: arXiv:2304.09573
status: public
title: 'Temperedness of locally symmetric spaces: The product case'
type: preprint
user_id: '45027'
year: '2023'
...
---
_id: '46147'
author:
- first_name: Anian
full_name: Brosch, Anian
id: '75779'
last_name: Brosch
orcid: 0000-0003-4871-1664
- first_name: Fabio
full_name: Tinazzi, Fabio
last_name: Tinazzi
- first_name: Oliver
full_name: Wallscheid, Oliver
id: '11291'
last_name: Wallscheid
orcid: https://orcid.org/0000-0001-9362-8777
- first_name: Mauro
full_name: Zigliotto, Mauro
last_name: Zigliotto
- first_name: Joachim
full_name: Böcker, Joachim
id: '66'
last_name: Böcker
orcid: 0000-0002-8480-7295
citation:
ama: Brosch A, Tinazzi F, Wallscheid O, Zigliotto M, Böcker J. Finite Set Sensorless
Control With Minimum a Priori Knowledge and Tuning Effort for Interior Permanent
Magnet Synchronous Motors. IEEE Transactions on Power Electronics. Published
online 2023. doi:10.1109/tpel.2023.3294557
apa: Brosch, A., Tinazzi, F., Wallscheid, O., Zigliotto, M., & Böcker, J. (2023).
Finite Set Sensorless Control With Minimum a Priori Knowledge and Tuning Effort
for Interior Permanent Magnet Synchronous Motors. IEEE Transactions on Power
Electronics. https://doi.org/10.1109/tpel.2023.3294557
bibtex: '@article{Brosch_Tinazzi_Wallscheid_Zigliotto_Böcker_2023, title={Finite
Set Sensorless Control With Minimum a Priori Knowledge and Tuning Effort for Interior
Permanent Magnet Synchronous Motors}, DOI={10.1109/tpel.2023.3294557},
journal={IEEE Transactions on Power Electronics}, publisher={Institute of Electrical
and Electronics Engineers (IEEE)}, author={Brosch, Anian and Tinazzi, Fabio and
Wallscheid, Oliver and Zigliotto, Mauro and Böcker, Joachim}, year={2023} }'
chicago: Brosch, Anian, Fabio Tinazzi, Oliver Wallscheid, Mauro Zigliotto, and Joachim
Böcker. “Finite Set Sensorless Control With Minimum a Priori Knowledge and Tuning
Effort for Interior Permanent Magnet Synchronous Motors.” IEEE Transactions
on Power Electronics, 2023. https://doi.org/10.1109/tpel.2023.3294557.
ieee: 'A. Brosch, F. Tinazzi, O. Wallscheid, M. Zigliotto, and J. Böcker, “Finite
Set Sensorless Control With Minimum a Priori Knowledge and Tuning Effort for Interior
Permanent Magnet Synchronous Motors,” IEEE Transactions on Power Electronics,
2023, doi: 10.1109/tpel.2023.3294557.'
mla: Brosch, Anian, et al. “Finite Set Sensorless Control With Minimum a Priori
Knowledge and Tuning Effort for Interior Permanent Magnet Synchronous Motors.”
IEEE Transactions on Power Electronics, Institute of Electrical and Electronics
Engineers (IEEE), 2023, doi:10.1109/tpel.2023.3294557.
short: A. Brosch, F. Tinazzi, O. Wallscheid, M. Zigliotto, J. Böcker, IEEE Transactions
on Power Electronics (2023).
date_created: 2023-07-25T20:33:12Z
date_updated: 2023-07-25T20:34:51Z
department:
- _id: '52'
doi: 10.1109/tpel.2023.3294557
keyword:
- Electrical and Electronic Engineering
language:
- iso: eng
publication: IEEE Transactions on Power Electronics
publication_identifier:
issn:
- 0885-8993
- 1941-0107
publication_status: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
status: public
title: Finite Set Sensorless Control With Minimum a Priori Knowledge and Tuning Effort
for Interior Permanent Magnet Synchronous Motors
type: journal_article
user_id: '75779'
year: '2023'
...
---
_id: '38041'
abstract:
- lang: eng
text: "While FPGA accelerator boards and their respective high-level design
tools are maturing, there is still a lack of multi-FPGA applications, libraries,
and not least, benchmarks and reference implementations towards sustained HPC
usage of these devices. As in the early days of GPUs in HPC, for workloads that
can reasonably be decoupled into loosely coupled working sets, multi-accelerator
support can be achieved by using standard communication interfaces like MPI on
the host side. However, for performance and productivity, some applications can
profit from a tighter coupling of the accelerators. FPGAs offer unique opportunities
here when extending the dataflow characteristics to their communication interfaces.\r\n
\ In this work, we extend the HPCC FPGA benchmark suite by multi-FPGA
support and three missing benchmarks that particularly characterize or stress
inter-device communication: b_eff, PTRANS, and LINPACK. With all benchmarks implemented
for current boards with Intel and Xilinx FPGAs, we established a baseline for
multi-FPGA performance. Additionally, for the communication-centric benchmarks,
we explored the potential of direct FPGA-to-FPGA communication with a circuit-switched
inter-FPGA network that is currently only available for one of the boards. The
evaluation with parallel execution on up to 26 FPGA boards makes use of one of
the largest academic FPGA installations."
author:
- first_name: Marius
full_name: Meyer, Marius
id: '40778'
last_name: Meyer
- first_name: Tobias
full_name: Kenter, Tobias
id: '3145'
last_name: Kenter
- first_name: Christian
full_name: Plessl, Christian
id: '16153'
last_name: Plessl
orcid: 0000-0001-5728-9982
citation:
ama: Meyer M, Kenter T, Plessl C. Multi-FPGA Designs and Scaling of HPC Challenge
Benchmarks via MPI and Circuit-Switched Inter-FPGA Networks. ACM Transactions
on Reconfigurable Technology and Systems. Published online 2023. doi:10.1145/3576200
apa: Meyer, M., Kenter, T., & Plessl, C. (2023). Multi-FPGA Designs and Scaling
of HPC Challenge Benchmarks via MPI and Circuit-Switched Inter-FPGA Networks.
ACM Transactions on Reconfigurable Technology and Systems. https://doi.org/10.1145/3576200
bibtex: '@article{Meyer_Kenter_Plessl_2023, title={Multi-FPGA Designs and Scaling
of HPC Challenge Benchmarks via MPI and Circuit-Switched Inter-FPGA Networks},
DOI={10.1145/3576200}, journal={ACM
Transactions on Reconfigurable Technology and Systems}, publisher={Association
for Computing Machinery (ACM)}, author={Meyer, Marius and Kenter, Tobias and Plessl,
Christian}, year={2023} }'
chicago: Meyer, Marius, Tobias Kenter, and Christian Plessl. “Multi-FPGA Designs
and Scaling of HPC Challenge Benchmarks via MPI and Circuit-Switched Inter-FPGA
Networks.” ACM Transactions on Reconfigurable Technology and Systems, 2023.
https://doi.org/10.1145/3576200.
ieee: 'M. Meyer, T. Kenter, and C. Plessl, “Multi-FPGA Designs and Scaling of HPC
Challenge Benchmarks via MPI and Circuit-Switched Inter-FPGA Networks,” ACM
Transactions on Reconfigurable Technology and Systems, 2023, doi: 10.1145/3576200.'
mla: Meyer, Marius, et al. “Multi-FPGA Designs and Scaling of HPC Challenge Benchmarks
via MPI and Circuit-Switched Inter-FPGA Networks.” ACM Transactions on Reconfigurable
Technology and Systems, Association for Computing Machinery (ACM), 2023, doi:10.1145/3576200.
short: M. Meyer, T. Kenter, C. Plessl, ACM Transactions on Reconfigurable Technology
and Systems (2023).
date_created: 2023-01-23T08:40:42Z
date_updated: 2023-07-28T08:02:05Z
department:
- _id: '27'
- _id: '518'
doi: 10.1145/3576200
keyword:
- General Computer Science
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://dl.acm.org/doi/10.1145/3576200
oa: '1'
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
- _id: '4'
name: 'SFB 901 - C: SFB 901 - Project Area C'
- _id: '1'
grant_number: '160364472'
name: 'SFB 901: SFB 901'
- _id: '14'
grant_number: '160364472'
name: 'SFB 901 - C2: SFB 901 - Subproject C2'
publication: ACM Transactions on Reconfigurable Technology and Systems
publication_identifier:
issn:
- 1936-7406
- 1936-7414
publication_status: published
publisher: Association for Computing Machinery (ACM)
quality_controlled: '1'
status: public
title: Multi-FPGA Designs and Scaling of HPC Challenge Benchmarks via MPI and Circuit-Switched
Inter-FPGA Networks
type: journal_article
user_id: '24135'
year: '2023'
...
---
_id: '46213'
author:
- first_name: Daniel
full_name: Weber, Daniel
last_name: Weber
- first_name: Maximilian
full_name: Schenke, Maximilian
last_name: Schenke
- first_name: Oliver
full_name: Wallscheid, Oliver
last_name: Wallscheid
citation:
ama: Weber D, Schenke M, Wallscheid O. Steady-State Error Compensation for Reinforcement
Learning-Based Control of Power Electronic Systems. IEEE Access. 2023;11:76524-76536.
doi:10.1109/access.2023.3297274
apa: Weber, D., Schenke, M., & Wallscheid, O. (2023). Steady-State Error Compensation
for Reinforcement Learning-Based Control of Power Electronic Systems. IEEE
Access, 11, 76524–76536. https://doi.org/10.1109/access.2023.3297274
bibtex: '@article{Weber_Schenke_Wallscheid_2023, title={Steady-State Error Compensation
for Reinforcement Learning-Based Control of Power Electronic Systems}, volume={11},
DOI={10.1109/access.2023.3297274},
journal={IEEE Access}, publisher={Institute of Electrical and Electronics Engineers
(IEEE)}, author={Weber, Daniel and Schenke, Maximilian and Wallscheid, Oliver},
year={2023}, pages={76524–76536} }'
chicago: 'Weber, Daniel, Maximilian Schenke, and Oliver Wallscheid. “Steady-State
Error Compensation for Reinforcement Learning-Based Control of Power Electronic
Systems.” IEEE Access 11 (2023): 76524–36. https://doi.org/10.1109/access.2023.3297274.'
ieee: 'D. Weber, M. Schenke, and O. Wallscheid, “Steady-State Error Compensation
for Reinforcement Learning-Based Control of Power Electronic Systems,” IEEE
Access, vol. 11, pp. 76524–76536, 2023, doi: 10.1109/access.2023.3297274.'
mla: Weber, Daniel, et al. “Steady-State Error Compensation for Reinforcement Learning-Based
Control of Power Electronic Systems.” IEEE Access, vol. 11, Institute of
Electrical and Electronics Engineers (IEEE), 2023, pp. 76524–36, doi:10.1109/access.2023.3297274.
short: D. Weber, M. Schenke, O. Wallscheid, IEEE Access 11 (2023) 76524–76536.
date_created: 2023-07-31T07:04:27Z
date_updated: 2023-07-31T07:04:48Z
department:
- _id: '34'
- _id: '52'
doi: 10.1109/access.2023.3297274
intvolume: ' 11'
keyword:
- General Engineering
- General Materials Science
- General Computer Science
- Electrical and Electronic Engineering
language:
- iso: eng
page: 76524-76536
publication: IEEE Access
publication_identifier:
issn:
- 2169-3536
publication_status: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
status: public
title: Steady-State Error Compensation for Reinforcement Learning-Based Control of
Power Electronic Systems
type: journal_article
user_id: '24041'
volume: 11
year: '2023'
...
---
_id: '46212'
author:
- first_name: Daniel
full_name: Weber, Daniel
last_name: Weber
- first_name: Maximilian
full_name: Schenke, Maximilian
last_name: Schenke
- first_name: Oliver
full_name: Wallscheid, Oliver
last_name: Wallscheid
citation:
ama: 'Weber D, Schenke M, Wallscheid O. Safe Reinforcement Learning-Based Control
in Power Electronic Systems. In: 2023 International Conference on Future Energy
Solutions (FES). IEEE; 2023. doi:10.1109/fes57669.2023.10182718'
apa: Weber, D., Schenke, M., & Wallscheid, O. (2023). Safe Reinforcement Learning-Based
Control in Power Electronic Systems. 2023 International Conference on Future
Energy Solutions (FES). https://doi.org/10.1109/fes57669.2023.10182718
bibtex: '@inproceedings{Weber_Schenke_Wallscheid_2023, title={Safe Reinforcement
Learning-Based Control in Power Electronic Systems}, DOI={10.1109/fes57669.2023.10182718},
booktitle={2023 International Conference on Future Energy Solutions (FES)}, publisher={IEEE},
author={Weber, Daniel and Schenke, Maximilian and Wallscheid, Oliver}, year={2023}
}'
chicago: Weber, Daniel, Maximilian Schenke, and Oliver Wallscheid. “Safe Reinforcement
Learning-Based Control in Power Electronic Systems.” In 2023 International
Conference on Future Energy Solutions (FES). IEEE, 2023. https://doi.org/10.1109/fes57669.2023.10182718.
ieee: 'D. Weber, M. Schenke, and O. Wallscheid, “Safe Reinforcement Learning-Based
Control in Power Electronic Systems,” 2023, doi: 10.1109/fes57669.2023.10182718.'
mla: Weber, Daniel, et al. “Safe Reinforcement Learning-Based Control in Power Electronic
Systems.” 2023 International Conference on Future Energy Solutions (FES),
IEEE, 2023, doi:10.1109/fes57669.2023.10182718.
short: 'D. Weber, M. Schenke, O. Wallscheid, in: 2023 International Conference on
Future Energy Solutions (FES), IEEE, 2023.'
date_created: 2023-07-31T07:01:22Z
date_updated: 2023-07-31T07:03:46Z
department:
- _id: '34'
- _id: '52'
doi: 10.1109/fes57669.2023.10182718
language:
- iso: eng
publication: 2023 International Conference on Future Energy Solutions (FES)
publication_status: published
publisher: IEEE
status: public
title: Safe Reinforcement Learning-Based Control in Power Electronic Systems
type: conference
user_id: '24041'
year: '2023'
...
---
_id: '46221'
author:
- first_name: N.
full_name: N., N.
last_name: N.
citation:
ama: N. N. Improving the End-of-Line Test of Custom-Built Geared Motors Using
Clustering Based on Neural Networks.; 2023.
apa: N., N. (2023). Improving the End-of-Line Test of Custom-Built Geared Motors
using Clustering based on Neural Networks.
bibtex: '@book{N._2023, title={Improving the End-of-Line Test of Custom-Built Geared
Motors using Clustering based on Neural Networks}, author={N., N.}, year={2023}
}'
chicago: N., N. Improving the End-of-Line Test of Custom-Built Geared Motors
Using Clustering Based on Neural Networks, 2023.
ieee: N. N., Improving the End-of-Line Test of Custom-Built Geared Motors using
Clustering based on Neural Networks. 2023.
mla: N., N. Improving the End-of-Line Test of Custom-Built Geared Motors Using
Clustering Based on Neural Networks. 2023.
short: N. N., Improving the End-of-Line Test of Custom-Built Geared Motors Using
Clustering Based on Neural Networks, 2023.
date_created: 2023-07-31T10:49:12Z
date_updated: 2023-07-31T10:49:30Z
department:
- _id: '79'
language:
- iso: eng
status: public
supervisor:
- first_name: Christian
full_name: Scheideler, Christian
id: '20792'
last_name: Scheideler
title: Improving the End-of-Line Test of Custom-Built Geared Motors using Clustering
based on Neural Networks
type: mastersthesis
user_id: '15504'
year: '2023'
...
---
_id: '46251'
author:
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
last_name: Ngonga Ngomo
citation:
ama: Demir C, Ngonga Ngomo A-C. Neuro-Symbolic Class Expression Learning. International
Joint Conference on Artificial Intelligence. Published online 2023.
apa: Demir, C., & Ngonga Ngomo, A.-C. (2023). Neuro-Symbolic Class Expression
Learning. International Joint Conference on Artificial Intelligence. International
Joint Conference on Artificial Intelligence IJCAI 2023, Macau.
bibtex: '@article{Demir_Ngonga Ngomo_2023, title={Neuro-Symbolic Class Expression
Learning}, journal={International Joint Conference on Artificial Intelligence},
author={Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}, year={2023} }'
chicago: Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “Neuro-Symbolic Class Expression
Learning.” International Joint Conference on Artificial Intelligence, 2023.
ieee: C. Demir and A.-C. Ngonga Ngomo, “Neuro-Symbolic Class Expression Learning,”
International Joint Conference on Artificial Intelligence, 2023.
mla: Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. “Neuro-Symbolic Class Expression
Learning.” International Joint Conference on Artificial Intelligence, 2023.
short: C. Demir, A.-C. Ngonga Ngomo, International Joint Conference on Artificial
Intelligence (2023).
conference:
location: Macau
name: International Joint Conference on Artificial Intelligence IJCAI 2023
date_created: 2023-08-01T09:30:37Z
date_updated: 2023-08-01T09:44:30Z
ddc:
- '000'
department:
- _id: '574'
file:
- access_level: open_access
content_type: application/pdf
creator: cdemir
date_created: 2023-08-01T09:30:35Z
date_updated: 2023-08-01T09:30:35Z
file_id: '46252'
file_name: public.pdf
file_size: 340865
relation: main_file
file_date_updated: 2023-08-01T09:30:35Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '407'
grant_number: '101070305'
name: 'ENEXA: Efficient Explainable Learning on Knowledge Graphs'
- _id: '410'
name: 'KnowGraphs: KnowGraphs: Knowledge Graphs at Scale'
- _id: '285'
grant_number: NW21-059D
name: 'SAIL: SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems'
publication: International Joint Conference on Artificial Intelligence
status: public
title: Neuro-Symbolic Class Expression Learning
type: journal_article
user_id: '43817'
year: '2023'
...
---
_id: '46256'
author:
- first_name: Yulai
full_name: Ma, Yulai
id: '92748'
last_name: Ma
- first_name: Davide
full_name: Mattiolo, Davide
last_name: Mattiolo
- first_name: Eckhard
full_name: Steffen, Eckhard
id: '15548'
last_name: Steffen
orcid: 0000-0002-9808-7401
- first_name: Isaak Hieronymus
full_name: Wolf, Isaak Hieronymus
id: '88145'
last_name: Wolf
citation:
ama: Ma Y, Mattiolo D, Steffen E, Wolf IH. Pairwise Disjoint Perfect Matchings in
r-Edge-Connected r-Regular Graphs. SIAM Journal on Discrete Mathematics.
2023;37(3):1548-1565. doi:10.1137/22m1500654
apa: Ma, Y., Mattiolo, D., Steffen, E., & Wolf, I. H. (2023). Pairwise Disjoint
Perfect Matchings in r-Edge-Connected r-Regular Graphs. SIAM Journal on Discrete
Mathematics, 37(3), 1548–1565. https://doi.org/10.1137/22m1500654
bibtex: '@article{Ma_Mattiolo_Steffen_Wolf_2023, title={Pairwise Disjoint Perfect
Matchings in r-Edge-Connected r-Regular Graphs}, volume={37}, DOI={10.1137/22m1500654},
number={3}, journal={SIAM Journal on Discrete Mathematics}, publisher={Society
for Industrial & Applied Mathematics (SIAM)}, author={Ma, Yulai and Mattiolo,
Davide and Steffen, Eckhard and Wolf, Isaak Hieronymus}, year={2023}, pages={1548–1565}
}'
chicago: 'Ma, Yulai, Davide Mattiolo, Eckhard Steffen, and Isaak Hieronymus Wolf.
“Pairwise Disjoint Perfect Matchings in R-Edge-Connected r-Regular Graphs.” SIAM
Journal on Discrete Mathematics 37, no. 3 (2023): 1548–65. https://doi.org/10.1137/22m1500654.'
ieee: 'Y. Ma, D. Mattiolo, E. Steffen, and I. H. Wolf, “Pairwise Disjoint Perfect
Matchings in r-Edge-Connected r-Regular Graphs,” SIAM Journal on Discrete Mathematics,
vol. 37, no. 3, pp. 1548–1565, 2023, doi: 10.1137/22m1500654.'
mla: Ma, Yulai, et al. “Pairwise Disjoint Perfect Matchings in R-Edge-Connected
r-Regular Graphs.” SIAM Journal on Discrete Mathematics, vol. 37, no. 3,
Society for Industrial & Applied Mathematics (SIAM), 2023, pp. 1548–65, doi:10.1137/22m1500654.
short: Y. Ma, D. Mattiolo, E. Steffen, I.H. Wolf, SIAM Journal on Discrete Mathematics
37 (2023) 1548–1565.
date_created: 2023-08-01T10:08:32Z
date_updated: 2023-08-01T10:09:35Z
department:
- _id: '542'
doi: 10.1137/22m1500654
intvolume: ' 37'
issue: '3'
keyword:
- General Mathematics
language:
- iso: eng
page: 1548-1565
publication: SIAM Journal on Discrete Mathematics
publication_identifier:
issn:
- 0895-4801
- 1095-7146
publication_status: published
publisher: Society for Industrial & Applied Mathematics (SIAM)
status: public
title: Pairwise Disjoint Perfect Matchings in r-Edge-Connected r-Regular Graphs
type: journal_article
user_id: '15540'
volume: 37
year: '2023'
...
---
_id: '43228'
abstract:
- lang: eng
text: "The computation of electron repulsion integrals (ERIs) over Gaussian-type
orbitals (GTOs) is a challenging problem in quantum-mechanics-based atomistic
simulations. In practical simulations, several trillions of ERIs may have to be\r\ncomputed
for every time step.\r\nIn this work, we investigate FPGAs as accelerators for
the ERI computation. We use template parameters, here within the Intel oneAPI
tool flow, to create customized designs for 256 different ERI quartet classes,
based on their orbitals. To maximize data reuse, all intermediates are buffered
in FPGA on-chip memory with customized layout. The pre-calculation of intermediates
also helps to overcome data dependencies caused by multi-dimensional recurrence\r\nrelations.
The involved loop structures are partially or even fully unrolled for high throughput
of FPGA kernels. Furthermore, a lossy compression algorithm utilizing arbitrary
bitwidth integers is integrated in the FPGA kernels. To our\r\nbest knowledge,
this is the first work on ERI computation on FPGAs that supports more than just
the single most basic quartet class. Also, the integration of ERI computation
and compression it a novelty that is not even covered by CPU or GPU libraries
so far.\r\nOur evaluation shows that using 16-bit integer for the ERI compression,
the fastest FPGA kernels exceed the performance of 10 GERIS ($10 \\times 10^9$
ERIs per second) on one Intel Stratix 10 GX 2800 FPGA, with maximum absolute errors
around $10^{-7}$ - $10^{-5}$ Hartree. The measured throughput can be accurately
explained by a performance model. The FPGA kernels deployed on 2 FPGAs outperform
similar computations using the widely used libint reference on a two-socket server
with 40 Xeon Gold 6148 CPU cores of the same process technology by factors up
to 6.0x and on a new two-socket server with 128 EPYC 7713 CPU cores by up to 1.9x."
author:
- first_name: Xin
full_name: Wu, Xin
id: '77439'
last_name: Wu
- first_name: Tobias
full_name: Kenter, Tobias
id: '3145'
last_name: Kenter
- first_name: Robert
full_name: Schade, Robert
id: '75963'
last_name: Schade
orcid: 0000-0002-6268-539
- first_name: Thomas
full_name: Kühne, Thomas
id: '49079'
last_name: Kühne
- first_name: Christian
full_name: Plessl, Christian
id: '16153'
last_name: Plessl
orcid: 0000-0001-5728-9982
citation:
ama: 'Wu X, Kenter T, Schade R, Kühne T, Plessl C. Computing and Compressing Electron
Repulsion Integrals on FPGAs. In: 2023 IEEE 31st Annual International Symposium
on Field-Programmable Custom Computing Machines (FCCM). ; 2023:162-173. doi:10.1109/FCCM57271.2023.00026'
apa: Wu, X., Kenter, T., Schade, R., Kühne, T., & Plessl, C. (2023). Computing
and Compressing Electron Repulsion Integrals on FPGAs. 2023 IEEE 31st Annual
International Symposium on Field-Programmable Custom Computing Machines (FCCM),
162–173. https://doi.org/10.1109/FCCM57271.2023.00026
bibtex: '@inproceedings{Wu_Kenter_Schade_Kühne_Plessl_2023, title={Computing and
Compressing Electron Repulsion Integrals on FPGAs}, DOI={10.1109/FCCM57271.2023.00026},
booktitle={2023 IEEE 31st Annual International Symposium on Field-Programmable
Custom Computing Machines (FCCM)}, author={Wu, Xin and Kenter, Tobias and Schade,
Robert and Kühne, Thomas and Plessl, Christian}, year={2023}, pages={162–173}
}'
chicago: Wu, Xin, Tobias Kenter, Robert Schade, Thomas Kühne, and Christian Plessl.
“Computing and Compressing Electron Repulsion Integrals on FPGAs.” In 2023
IEEE 31st Annual International Symposium on Field-Programmable Custom Computing
Machines (FCCM), 162–73, 2023. https://doi.org/10.1109/FCCM57271.2023.00026.
ieee: 'X. Wu, T. Kenter, R. Schade, T. Kühne, and C. Plessl, “Computing and Compressing
Electron Repulsion Integrals on FPGAs,” in 2023 IEEE 31st Annual International
Symposium on Field-Programmable Custom Computing Machines (FCCM), 2023, pp.
162–173, doi: 10.1109/FCCM57271.2023.00026.'
mla: Wu, Xin, et al. “Computing and Compressing Electron Repulsion Integrals on
FPGAs.” 2023 IEEE 31st Annual International Symposium on Field-Programmable
Custom Computing Machines (FCCM), 2023, pp. 162–73, doi:10.1109/FCCM57271.2023.00026.
short: 'X. Wu, T. Kenter, R. Schade, T. Kühne, C. Plessl, in: 2023 IEEE 31st Annual
International Symposium on Field-Programmable Custom Computing Machines (FCCM),
2023, pp. 162–173.'
date_created: 2023-03-30T11:15:40Z
date_updated: 2023-08-02T15:05:42Z
department:
- _id: '27'
- _id: '518'
doi: 10.1109/FCCM57271.2023.00026
external_id:
arxiv:
- '2303.13632'
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/10171537
page: 162-173
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: 2023 IEEE 31st Annual International Symposium on Field-Programmable Custom
Computing Machines (FCCM)
quality_controlled: '1'
status: public
title: Computing and Compressing Electron Repulsion Integrals on FPGAs
type: conference
user_id: '75963'
year: '2023'
...
---
_id: '45361'
abstract:
- lang: eng
text: The non-orthogonal local submatrix method applied to electronic structure–based
molecular dynamics simulations is shown to exceed 1.1 EFLOP/s in FP16/FP32-mixed
floating-point arithmetic when using 4400 NVIDIA A100 GPUs of the Perlmutter system.
This is enabled by a modification of the original method that pushes the sustained
fraction of the peak performance to about 80%. Example calculations are performed
for SARS-CoV-2 spike proteins with up to 83 million atoms.
article_number: '109434202311776'
article_type: original
author:
- first_name: Robert
full_name: Schade, Robert
id: '75963'
last_name: Schade
orcid: 0000-0002-6268-539
- first_name: Tobias
full_name: Kenter, Tobias
id: '3145'
last_name: Kenter
- first_name: Hossam
full_name: Elgabarty, Hossam
id: '60250'
last_name: Elgabarty
orcid: 0000-0002-4945-1481
- first_name: Michael
full_name: Lass, Michael
id: '24135'
last_name: Lass
orcid: 0000-0002-5708-7632
- first_name: Thomas
full_name: Kühne, Thomas
id: '49079'
last_name: Kühne
- first_name: Christian
full_name: Plessl, Christian
id: '16153'
last_name: Plessl
orcid: 0000-0001-5728-9982
citation:
ama: Schade R, Kenter T, Elgabarty H, Lass M, Kühne T, Plessl C. Breaking the exascale
barrier for the electronic structure problem in ab-initio molecular dynamics.
The International Journal of High Performance Computing Applications. Published
online 2023. doi:10.1177/10943420231177631
apa: Schade, R., Kenter, T., Elgabarty, H., Lass, M., Kühne, T., & Plessl, C.
(2023). Breaking the exascale barrier for the electronic structure problem in
ab-initio molecular dynamics. The International Journal of High Performance
Computing Applications, Article 109434202311776. https://doi.org/10.1177/10943420231177631
bibtex: '@article{Schade_Kenter_Elgabarty_Lass_Kühne_Plessl_2023, title={Breaking
the exascale barrier for the electronic structure problem in ab-initio molecular
dynamics}, DOI={10.1177/10943420231177631},
number={109434202311776}, journal={The International Journal of High Performance
Computing Applications}, publisher={SAGE Publications}, author={Schade, Robert
and Kenter, Tobias and Elgabarty, Hossam and Lass, Michael and Kühne, Thomas and
Plessl, Christian}, year={2023} }'
chicago: Schade, Robert, Tobias Kenter, Hossam Elgabarty, Michael Lass, Thomas Kühne,
and Christian Plessl. “Breaking the Exascale Barrier for the Electronic Structure
Problem in Ab-Initio Molecular Dynamics.” The International Journal of High
Performance Computing Applications, 2023. https://doi.org/10.1177/10943420231177631.
ieee: 'R. Schade, T. Kenter, H. Elgabarty, M. Lass, T. Kühne, and C. Plessl, “Breaking
the exascale barrier for the electronic structure problem in ab-initio molecular
dynamics,” The International Journal of High Performance Computing Applications,
Art. no. 109434202311776, 2023, doi: 10.1177/10943420231177631.'
mla: Schade, Robert, et al. “Breaking the Exascale Barrier for the Electronic Structure
Problem in Ab-Initio Molecular Dynamics.” The International Journal of High
Performance Computing Applications, 109434202311776, SAGE Publications, 2023,
doi:10.1177/10943420231177631.
short: R. Schade, T. Kenter, H. Elgabarty, M. Lass, T. Kühne, C. Plessl, The International
Journal of High Performance Computing Applications (2023).
date_created: 2023-05-30T09:19:09Z
date_updated: 2023-08-02T15:04:53Z
department:
- _id: '27'
- _id: '518'
doi: 10.1177/10943420231177631
keyword:
- Hardware and Architecture
- Theoretical Computer Science
- Software
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://journals.sagepub.com/doi/10.1177/10943420231177631
oa: '1'
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: The International Journal of High Performance Computing Applications
publication_identifier:
issn:
- 1094-3420
- 1741-2846
publication_status: published
publisher: SAGE Publications
quality_controlled: '1'
status: public
title: Breaking the exascale barrier for the electronic structure problem in ab-initio
molecular dynamics
type: journal_article
user_id: '75963'
year: '2023'
...
---
_id: '45780'
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
citation:
ama: 'Tornede A. Advanced Algorithm Selection with Machine Learning: Handling
Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions.;
2023. doi:10.17619/UNIPB/1-1780
'
apa: 'Tornede, A. (2023). Advanced Algorithm Selection with Machine Learning:
Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta
Level Decisions. https://doi.org/10.17619/UNIPB/1-1780
'
bibtex: '@book{Tornede_2023, title={Advanced Algorithm Selection with Machine Learning:
Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta
Level Decisions}, DOI={10.17619/UNIPB/1-1780
}, author={Tornede, Alexander}, year={2023} }'
chicago: 'Tornede, Alexander. Advanced Algorithm Selection with Machine Learning:
Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta
Level Decisions, 2023. https://doi.org/10.17619/UNIPB/1-1780
.'
ieee: 'A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling
Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions.
2023.'
mla: 'Tornede, Alexander. Advanced Algorithm Selection with Machine Learning:
Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta
Level Decisions. 2023, doi:10.17619/UNIPB/1-1780 .'
short: 'A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling
Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions,
2023.'
date_created: 2023-06-27T05:20:14Z
date_updated: 2023-08-04T06:01:49Z
ddc:
- '006'
department:
- _id: '355'
doi: '10.17619/UNIPB/1-1780 '
file:
- access_level: open_access
content_type: application/pdf
creator: ahetzer
date_created: 2023-07-24T08:40:35Z
date_updated: 2023-07-24T08:42:01Z
file_id: '46118'
file_name: dissertation_alexander_tornede_final_publishing_compressed.pdf
file_size: 4300633
relation: main_file
title: ' Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm
Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions'
file_date_updated: 2023-07-24T08:42:01Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '10'
grant_number: '160364472'
name: 'SFB 901 - B2: Konfiguration und Bewertung (B02)'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '1'
grant_number: '160364472'
name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen
in dynamischen Märkten '
status: public
supervisor:
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
title: 'Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm
Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions'
type: dissertation
user_id: '15504'
year: '2023'
...
---
_id: '29240'
abstract:
- lang: eng
text: "The principle of least action is one of the most fundamental physical principle.
It says that among all possible motions connecting two points in a phase space,
the system will exhibit those motions which extremise an action functional. Many
qualitative features of dynamical systems, such as the presence of conservation
laws and energy balance equations, are related to the existence of an action functional.
Incorporating variational structure into learning algorithms for dynamical systems
is, therefore, crucial in order to make sure that the learned model shares important
features with the exact physical system. In this paper we show how to incorporate
variational principles into trajectory predictions of learned dynamical systems.
The novelty of this work is that (1) our technique relies only on discrete position
data of observed trajectories. Velocities or conjugate momenta do not need to
be observed or approximated and no prior knowledge about the form of the variational
principle is assumed. Instead, they are recovered using backward error analysis.
(2) Moreover, our technique compensates discretisation errors when trajectories
are computed from the learned system. This is important when moderate to large
step-sizes are used and high accuracy is required. For this,\r\nwe introduce and
rigorously analyse the concept of inverse modified Lagrangians by developing an
inverse version of variational backward error analysis. (3) Finally, we introduce
a method to perform system identification from position observations only, based
on variational backward error analysis."
article_type: original
author:
- first_name: Sina
full_name: Ober-Blöbaum, Sina
id: '16494'
last_name: Ober-Blöbaum
- first_name: Christian
full_name: Offen, Christian
id: '85279'
last_name: Offen
orcid: 0000-0002-5940-8057
citation:
ama: Ober-Blöbaum S, Offen C. Variational Learning of Euler–Lagrange Dynamics from
Data. Journal of Computational and Applied Mathematics. 2023;421:114780.
doi:10.1016/j.cam.2022.114780
apa: Ober-Blöbaum, S., & Offen, C. (2023). Variational Learning of Euler–Lagrange
Dynamics from Data. Journal of Computational and Applied Mathematics, 421,
114780. https://doi.org/10.1016/j.cam.2022.114780
bibtex: '@article{Ober-Blöbaum_Offen_2023, title={Variational Learning of Euler–Lagrange
Dynamics from Data}, volume={421}, DOI={10.1016/j.cam.2022.114780},
journal={Journal of Computational and Applied Mathematics}, publisher={Elsevier},
author={Ober-Blöbaum, Sina and Offen, Christian}, year={2023}, pages={114780}
}'
chicago: 'Ober-Blöbaum, Sina, and Christian Offen. “Variational Learning of Euler–Lagrange
Dynamics from Data.” Journal of Computational and Applied Mathematics 421
(2023): 114780. https://doi.org/10.1016/j.cam.2022.114780.'
ieee: 'S. Ober-Blöbaum and C. Offen, “Variational Learning of Euler–Lagrange Dynamics
from Data,” Journal of Computational and Applied Mathematics, vol. 421,
p. 114780, 2023, doi: 10.1016/j.cam.2022.114780.'
mla: Ober-Blöbaum, Sina, and Christian Offen. “Variational Learning of Euler–Lagrange
Dynamics from Data.” Journal of Computational and Applied Mathematics,
vol. 421, Elsevier, 2023, p. 114780, doi:10.1016/j.cam.2022.114780.
short: S. Ober-Blöbaum, C. Offen, Journal of Computational and Applied Mathematics
421 (2023) 114780.
date_created: 2022-01-11T13:24:00Z
date_updated: 2023-08-10T08:42:39Z
ddc:
- '510'
department:
- _id: '636'
doi: 10.1016/j.cam.2022.114780
external_id:
arxiv:
- '2112.12619'
file:
- access_level: open_access
content_type: application/pdf
creator: coffen
date_created: 2022-06-28T15:25:50Z
date_updated: 2022-06-28T15:25:50Z
description: |-
The principle of least action is one of the most fundamental physical principle. It says that among all possible motions
connecting two points in a phase space, the system will exhibit those motions which extremise an action functional.
Many qualitative features of dynamical systems, such as the presence of conservation laws and energy balance equa-
tions, are related to the existence of an action functional. Incorporating variational structure into learning algorithms
for dynamical systems is, therefore, crucial in order to make sure that the learned model shares important features
with the exact physical system. In this paper we show how to incorporate variational principles into trajectory predic-
tions of learned dynamical systems. The novelty of this work is that (1) our technique relies only on discrete position
data of observed trajectories. Velocities or conjugate momenta do not need to be observed or approximated and no
prior knowledge about the form of the variational principle is assumed. Instead, they are recovered using backward
error analysis. (2) Moreover, our technique compensates discretisation errors when trajectories are computed from the
learned system. This is important when moderate to large step-sizes are used and high accuracy is required. For this,
we introduce and rigorously analyse the concept of inverse modified Lagrangians by developing an inverse version of
variational backward error analysis. (3) Finally, we introduce a method to perform system identification from position
observations only, based on variational backward error analysis.
file_id: '32274'
file_name: ShadowLagrangian_revision1_journal_style_arxiv.pdf
file_size: 3640770
relation: main_file
title: Variational Learning of Euler–Lagrange Dynamics from Data
file_date_updated: 2022-06-28T15:25:50Z
has_accepted_license: '1'
intvolume: ' 421'
keyword:
- Lagrangian learning
- variational backward error analysis
- modified Lagrangian
- variational integrators
- physics informed learning
language:
- iso: eng
oa: '1'
page: '114780'
publication: Journal of Computational and Applied Mathematics
publication_identifier:
issn:
- 0377-0427
publication_status: epub_ahead
publisher: Elsevier
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/Christian-Offen/LagrangianShadowIntegration
status: public
title: Variational Learning of Euler–Lagrange Dynamics from Data
type: journal_article
user_id: '85279'
volume: 421
year: '2023'
...
---
_id: '29236'
abstract:
- lang: eng
text: The numerical solution of an ordinary differential equation can be interpreted
as the exact solution of a nearby modified equation. Investigating the behaviour
of numerical solutions by analysing the modified equation is known as backward
error analysis. If the original and modified equation share structural properties,
then the exact and approximate solution share geometric features such as the existence
of conserved quantities. Conjugate symplectic methods preserve a modified symplectic
form and a modified Hamiltonian when applied to a Hamiltonian system. We show
how a blended version of variational and symplectic techniques can be used to
compute modified symplectic and Hamiltonian structures. In contrast to other approaches,
our backward error analysis method does not rely on an ansatz but computes the
structures systematically, provided that a variational formulation of the method
is known. The technique is illustrated on the example of symmetric linear multistep
methods with matrix coefficients.
article_type: original
author:
- first_name: Robert
full_name: McLachlan, Robert
last_name: McLachlan
- first_name: Christian
full_name: Offen, Christian
id: '85279'
last_name: Offen
orcid: 0000-0002-5940-8057
citation:
ama: McLachlan R, Offen C. Backward error analysis for conjugate symplectic methods.
Journal of Geometric Mechanics. 2023;15(1):98-115. doi:10.3934/jgm.2023005
apa: McLachlan, R., & Offen, C. (2023). Backward error analysis for conjugate
symplectic methods. Journal of Geometric Mechanics, 15(1), 98–115.
https://doi.org/10.3934/jgm.2023005
bibtex: '@article{McLachlan_Offen_2023, title={Backward error analysis for conjugate
symplectic methods}, volume={15}, DOI={10.3934/jgm.2023005},
number={1}, journal={Journal of Geometric Mechanics}, publisher={AIMS Press},
author={McLachlan, Robert and Offen, Christian}, year={2023}, pages={98–115} }'
chicago: 'McLachlan, Robert, and Christian Offen. “Backward Error Analysis for Conjugate
Symplectic Methods.” Journal of Geometric Mechanics 15, no. 1 (2023): 98–115.
https://doi.org/10.3934/jgm.2023005.'
ieee: 'R. McLachlan and C. Offen, “Backward error analysis for conjugate symplectic
methods,” Journal of Geometric Mechanics, vol. 15, no. 1, pp. 98–115, 2023,
doi: 10.3934/jgm.2023005.'
mla: McLachlan, Robert, and Christian Offen. “Backward Error Analysis for Conjugate
Symplectic Methods.” Journal of Geometric Mechanics, vol. 15, no. 1, AIMS
Press, 2023, pp. 98–115, doi:10.3934/jgm.2023005.
short: R. McLachlan, C. Offen, Journal of Geometric Mechanics 15 (2023) 98–115.
date_created: 2022-01-11T12:48:39Z
date_updated: 2023-08-10T08:40:30Z
ddc:
- '510'
department:
- _id: '636'
doi: 10.3934/jgm.2023005
external_id:
arxiv:
- '2201.03911'
file:
- access_level: open_access
content_type: application/pdf
creator: coffen
date_created: 2022-08-12T16:48:59Z
date_updated: 2022-08-12T16:48:59Z
description: The numerical solution of an ordinary differential equation can be
interpreted as the exact solution of a nearby modified equation. Investigating
the behaviour of numerical solutions by analysing the modified equation is known
as backward error analysis. If the original and modified equation share structural
properties, then the exact and approximate solution share geometric features such
as the existence of conserved quantities. Conjugate symplectic methods preserve
a modified symplectic form and a modified Hamiltonian when applied to a Hamiltonian
system. We show how a blended version of variational and symplectic techniques
can be used to compute modified symplectic and Hamiltonian structures. In contrast
to other approaches, our backward error analysis method does not rely on an ansatz
but computes the structures systematically, provided that a variational formulation
of the method is known. The technique is illustrated on the example of symmetric
linear multistep methods with matrix coefficients.
file_id: '32801'
file_name: BEA_MultiStep_Matrix.pdf
file_size: 827030
relation: main_file
title: Backward error analysis for conjugate symplectic methods
file_date_updated: 2022-08-12T16:48:59Z
has_accepted_license: '1'
intvolume: ' 15'
issue: '1'
keyword:
- variational integrators
- backward error analysis
- Euler--Lagrange equations
- multistep methods
- conjugate symplectic methods
language:
- iso: eng
oa: '1'
page: 98-115
publication: Journal of Geometric Mechanics
publication_status: published
publisher: AIMS Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/Christian-Offen/BEAConjugateSymplectic
status: public
title: Backward error analysis for conjugate symplectic methods
type: journal_article
user_id: '85279'
volume: 15
year: '2023'
...
---
_id: '37654'
abstract:
- lang: eng
text: "Recently, Hamiltonian neural networks (HNN) have been introduced to incorporate
prior physical knowledge when\r\nlearning the dynamical equations of Hamiltonian
systems. Hereby, the symplectic system structure is preserved despite\r\nthe data-driven
modeling approach. However, preserving symmetries requires additional attention.
In this research, we\r\nenhance the HNN with a Lie algebra framework to detect
and embed symmetries in the neural network. This approach\r\nallows to simultaneously
learn the symmetry group action and the total energy of the system. As illustrating
examples,\r\na pendulum on a cart and a two-body problem from astrodynamics are
considered."
article_number: '063115'
article_type: original
author:
- first_name: Eva
full_name: Dierkes, Eva
last_name: Dierkes
- first_name: Christian
full_name: Offen, Christian
id: '85279'
last_name: Offen
orcid: 0000-0002-5940-8057
- first_name: Sina
full_name: Ober-Blöbaum, Sina
id: '16494'
last_name: Ober-Blöbaum
- first_name: Kathrin
full_name: Flaßkamp, Kathrin
last_name: Flaßkamp
citation:
ama: Dierkes E, Offen C, Ober-Blöbaum S, Flaßkamp K. Hamiltonian Neural Networks
with Automatic Symmetry Detection. Chaos. 2023;33(6). doi:10.1063/5.0142969
apa: Dierkes, E., Offen, C., Ober-Blöbaum, S., & Flaßkamp, K. (2023). Hamiltonian
Neural Networks with Automatic Symmetry Detection. Chaos, 33(6),
Article 063115. https://doi.org/10.1063/5.0142969
bibtex: '@article{Dierkes_Offen_Ober-Blöbaum_Flaßkamp_2023, title={Hamiltonian Neural
Networks with Automatic Symmetry Detection}, volume={33}, DOI={10.1063/5.0142969},
number={6063115}, journal={Chaos}, publisher={AIP Publishing}, author={Dierkes,
Eva and Offen, Christian and Ober-Blöbaum, Sina and Flaßkamp, Kathrin}, year={2023}
}'
chicago: Dierkes, Eva, Christian Offen, Sina Ober-Blöbaum, and Kathrin Flaßkamp.
“Hamiltonian Neural Networks with Automatic Symmetry Detection.” Chaos
33, no. 6 (2023). https://doi.org/10.1063/5.0142969.
ieee: 'E. Dierkes, C. Offen, S. Ober-Blöbaum, and K. Flaßkamp, “Hamiltonian Neural
Networks with Automatic Symmetry Detection,” Chaos, vol. 33, no. 6, Art.
no. 063115, 2023, doi: 10.1063/5.0142969.'
mla: Dierkes, Eva, et al. “Hamiltonian Neural Networks with Automatic Symmetry Detection.”
Chaos, vol. 33, no. 6, 063115, AIP Publishing, 2023, doi:10.1063/5.0142969.
short: E. Dierkes, C. Offen, S. Ober-Blöbaum, K. Flaßkamp, Chaos 33 (2023).
date_created: 2023-01-20T09:10:06Z
date_updated: 2023-08-10T08:37:01Z
ddc:
- '510'
department:
- _id: '636'
doi: 10.1063/5.0142969
external_id:
arxiv:
- '2301.07928'
file:
- access_level: open_access
content_type: application/pdf
creator: coffen
date_created: 2023-04-26T16:20:56Z
date_updated: 2023-04-26T16:20:56Z
description: |-
Incorporating physical system knowledge into data-driven
system identification has been shown to be beneficial. The
approach presented in this article combines learning of an
energy-conserving model from data with detecting a Lie
group representation of the unknown system symmetry.
The proposed approach can improve the learned model
and reveal underlying symmetry simultaneously.
file_id: '44205'
file_name: JournalPaper_main.pdf
file_size: 5200111
relation: main_file
title: Hamiltonian Neural Networks with Automatic Symmetry Detection
file_date_updated: 2023-04-26T16:20:56Z
has_accepted_license: '1'
intvolume: ' 33'
issue: '6'
language:
- iso: eng
oa: '1'
publication: Chaos
publication_identifier:
issn:
- 1054-1500
publication_status: published
publisher: AIP Publishing
related_material:
link:
- description: GitHub
relation: software
url: https://github.com/eva-dierkes/HNN_withSymmetries
status: public
title: Hamiltonian Neural Networks with Automatic Symmetry Detection
type: journal_article
user_id: '85279'
volume: 33
year: '2023'
...
---
_id: '46482'
abstract:
- lang: eng
text: "Ever increasing demands on the performance of microchips are leading to ever
more complex semiconductor technologies with ever shrinking feature sizes. Complex
applications with high demands on safety and reliability, such as autonomous driving,
are simultaneously driving the requirements for test and diagnosis of VLSI circuits.
Throughout the life cycle of a microchip, uncertainties occur that affect its
timing behavior. For example, weak circuit structures, aging effects, or process
variations can lead to a change in the timing behavior of the circuit. While these
uncertainties do not necessarily lead to a change of the functional behavior,
they can lead to a reliability problem.\r\nWith modular and hybrid compaction
two test instruments are presented in this work that can be used for X-tolerant
test response compaction in the built-in Faster-than-At-Speed Test (FAST) which
is used to detect uncertainties in VLSI circuits. One challenge for test response
compaction during FAST is the high and varying X-rate at the outputs of the circuit
under test. By dividing the circuit outputs into test groups and separately compacting
these test groups using stochastic compactors, the modular compaction is able
to handle these high and varying X-rates.\r\nTo deal with uncertainties on logic
interconnects, a method for distinguishing crosstalk and process variation is
presented. In current semiconductor technologies, the number of parasitic coupling
capacitances between logic interconnects is growing. These coupling capacitances
can lead to crosstalk, which causes increased current flow in the logic interconnects,
which in turn can lead to increased electromigration. In the presented method,
delay maps describing the timing behavior of the circuit outputs at different
operating points are used to train artificial neural networks which classify the
tested circuits into fault-free and faulty."
- lang: ger
text: "Immer größere Anforderungen an die Leistungsfähigkeit von Mikrochips führen
zu Halbleitertechnologien mit immer kleiner werdenden Strukturgrößen. Anwendungen
mit hohen Ansprüchen an Sicherheit und Zuverlässigkeit, wie z.B. das autonome
Fahren, treiben gleichzeitig die Anforderungen an den Test hochintegrierter Schaltungen
an. Während des gesamten Lebenszyklus eines Mikrochips kommt es zu Unsicherheiten
im Zeitverhalten. So können z.B. schwache Schaltungsstrukturen, Alterungseffekte
oder Prozessvariationen zu einer Veränderung des Zeitverhaltens führen. Während
diese Unsicherheiten nicht zu einer Veränderung des funktionalen Verhaltens führen
müssen, können sie jedoch zu einem Zuverlässigkeitsproblem führen.\r\nMit der
modularen und der hybriden Kompaktierung werden in dieser Arbeit zwei Testinstrumente
vorgestellt, die für die X-tolerante Testantwortkompaktierung im eingebauten Hochgeschwindigkeitstest
verwendet werden können. Eine Herausforderung für die Testantwortkompaktierung
während des Hochgeschwindigkeitstests ist die hohe und variierende X-Rate an den
Ausgängen der zu testenden Schaltung. Durch die Einteilung der Schaltungsausgänge
in Prüfgruppen und die separierte Kompaktierung der Prüfgruppen mithilfe von stochastischen
Kompaktierern, können die vorgestellten Verfahren diese hohen und variierenden
X-Raten verarbeiten.\r\nFür den Umgang mit Unsicherheiten auf Verbindungsleitungen
der Logik-Schaltung wird ein Verfahren zur Unterscheidung von Übersprechen und
Prozessvariation vorgestellt. In aktuellen Halbleitertechnologien kommt es vermehrt
zu parasitären Koppelkapazitäten zwischen den Verbindungsleitungen. In dem vorgestellten
Verfahren werden künstliche neuronale Netze trainiert, um die Schaltungen in fehlerfrei
und fehlerhaft zu klassifizieren."
author:
- first_name: Alexander
full_name: Sprenger, Alexander
id: '22707'
last_name: Sprenger
orcid: 0000-0002-0775-7677
citation:
ama: Sprenger A. Testinstrumente und Testdatenanalyse zur Verarbeitung von Unsicherheiten
in Logikblöcken hochintegrierter Schaltungen. Universität Paderborn; 2023.
doi:10.17619/UNIPB/1-1787
apa: Sprenger, A. (2023). Testinstrumente und Testdatenanalyse zur Verarbeitung
von Unsicherheiten in Logikblöcken hochintegrierter Schaltungen. Universität
Paderborn. https://doi.org/10.17619/UNIPB/1-1787
bibtex: '@book{Sprenger_2023, place={Paderborn}, title={Testinstrumente und Testdatenanalyse
zur Verarbeitung von Unsicherheiten in Logikblöcken hochintegrierter Schaltungen},
DOI={10.17619/UNIPB/1-1787},
publisher={Universität Paderborn}, author={Sprenger, Alexander}, year={2023} }'
chicago: 'Sprenger, Alexander. Testinstrumente und Testdatenanalyse zur Verarbeitung
von Unsicherheiten in Logikblöcken hochintegrierter Schaltungen. Paderborn:
Universität Paderborn, 2023. https://doi.org/10.17619/UNIPB/1-1787.'
ieee: 'A. Sprenger, Testinstrumente und Testdatenanalyse zur Verarbeitung von
Unsicherheiten in Logikblöcken hochintegrierter Schaltungen. Paderborn: Universität
Paderborn, 2023.'
mla: Sprenger, Alexander. Testinstrumente und Testdatenanalyse zur Verarbeitung
von Unsicherheiten in Logikblöcken hochintegrierter Schaltungen. Universität
Paderborn, 2023, doi:10.17619/UNIPB/1-1787.
short: A. Sprenger, Testinstrumente und Testdatenanalyse zur Verarbeitung von Unsicherheiten
in Logikblöcken hochintegrierter Schaltungen, Universität Paderborn, Paderborn,
2023.
date_created: 2023-08-12T09:10:38Z
date_updated: 2023-08-12T09:13:18Z
department:
- _id: '48'
doi: 10.17619/UNIPB/1-1787
extern: '1'
keyword:
- Testantwortkompaktierung
- Prozessvariation
- Silicon Lifecycle Management
language:
- iso: ger
main_file_link:
- open_access: '1'
url: https://nbn-resolving.org/urn:nbn:de:hbz:466:2-45493
oa: '1'
page: xi, 160
place: Paderborn
publication_status: published
publisher: Universität Paderborn
status: public
supervisor:
- first_name: Sybille
full_name: Hellebrand, Sybille
id: '209'
last_name: Hellebrand
orcid: 0000-0002-3717-3939
- first_name: Marco
full_name: Platzner, Marco
id: '398'
last_name: Platzner
title: Testinstrumente und Testdatenanalyse zur Verarbeitung von Unsicherheiten in
Logikblöcken hochintegrierter Schaltungen
type: dissertation
user_id: '22707'
year: '2023'
...
---
_id: '45558'
abstract:
- lang: eng
text: Graffiti is an urban phenomenon that is increasingly attracting the interest
of the sciences. To the best of our knowledge, no suitable data corpora are available
for systematic research until now. The Information System Graffiti in Germany
project (Ingrid) closes this gap by dealing with graffiti image collections that
have been made available to the project for public use. Within Ingrid, the graffiti
images are collected, digitized and annotated. With this work, we aim to support
the rapid access to a comprehensive data source on Ingrid targeted especially
by researchers. In particular, we present IngridKG, an RDF knowledge graph of
annotated graffiti, abides by the Linked Data and FAIR principles. We weekly update
IngridKG by augmenting the new annotated graffiti to our knowledge graph. Our
generation pipeline applies RDF data conversion, link discovery and data fusion
approaches to the original data. The current version of IngridKG contains 460,640,154
triples and is linked to 3 other knowledge graphs by over 200,000 links. In our
use case studies, we demonstrate the usefulness of our knowledge graph for different
applications.
author:
- first_name: Mohamed
full_name: Sherif, Mohamed
id: '67234'
last_name: Sherif
orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Ana Alexandra
full_name: Morim da Silva, Ana Alexandra
last_name: Morim da Silva
- first_name: Svetlana
full_name: Pestryakova, Svetlana
last_name: Pestryakova
- first_name: Abdullah Fathi Ahmed
full_name: Ahmed, Abdullah Fathi Ahmed
id: '29670'
last_name: Ahmed
- first_name: Sven
full_name: Niemann, Sven
id: '6593'
last_name: Niemann
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Sherif M, Morim da Silva AA, Pestryakova S, Ahmed AFA, Niemann S, Ngonga Ngomo
A-C. IngridKG: A FAIR Knowledge Graph of Graffiti. LibreCat University;
2023. doi:10.5281/ZENODO.7560242'
apa: 'Sherif, M., Morim da Silva, A. A., Pestryakova, S., Ahmed, A. F. A., Niemann,
S., & Ngonga Ngomo, A.-C. (2023). IngridKG: A FAIR Knowledge Graph of Graffiti.
LibreCat University. https://doi.org/10.5281/ZENODO.7560242'
bibtex: '@book{Sherif_Morim da Silva_Pestryakova_Ahmed_Niemann_Ngonga Ngomo_2023,
title={IngridKG: A FAIR Knowledge Graph of Graffiti}, DOI={10.5281/ZENODO.7560242},
publisher={LibreCat University}, author={Sherif, Mohamed and Morim da Silva, Ana
Alexandra and Pestryakova, Svetlana and Ahmed, Abdullah Fathi Ahmed and Niemann,
Sven and Ngonga Ngomo, Axel-Cyrille}, year={2023} }'
chicago: 'Sherif, Mohamed, Ana Alexandra Morim da Silva, Svetlana Pestryakova, Abdullah
Fathi Ahmed Ahmed, Sven Niemann, and Axel-Cyrille Ngonga Ngomo. IngridKG: A
FAIR Knowledge Graph of Graffiti. LibreCat University, 2023. https://doi.org/10.5281/ZENODO.7560242.'
ieee: 'M. Sherif, A. A. Morim da Silva, S. Pestryakova, A. F. A. Ahmed, S. Niemann,
and A.-C. Ngonga Ngomo, IngridKG: A FAIR Knowledge Graph of Graffiti. LibreCat
University, 2023.'
mla: 'Sherif, Mohamed, et al. IngridKG: A FAIR Knowledge Graph of Graffiti.
LibreCat University, 2023, doi:10.5281/ZENODO.7560242.'
short: 'M. Sherif, A.A. Morim da Silva, S. Pestryakova, A.F.A. Ahmed, S. Niemann,
A.-C. Ngonga Ngomo, IngridKG: A FAIR Knowledge Graph of Graffiti, LibreCat University,
2023.'
date_created: 2023-06-09T10:09:34Z
date_updated: 2023-08-16T10:23:55Z
department:
- _id: '34'
doi: 10.5281/ZENODO.7560242
project:
- _id: '104'
grant_number: '289287267'
name: 'INGRID: INGRID: Informationssystem Graffiti in Deutschland'
publisher: LibreCat University
status: public
title: 'IngridKG: A FAIR Knowledge Graph of Graffiti'
type: research_data
user_id: '67234'
year: '2023'
...
---
_id: '46579'
abstract:
- lang: eng
text: "The Koopman operator has become an essential tool for data-driven analysis,
prediction and control of complex systems, the main reason being the enormous
potential of identifying linear function space representations of nonlinear\r\ndynamics
from measurements. Until now, the situation where for large-scale systems, we
(i) only have access to partial observations (i.e., measurements, as is very common
for experimental data) or (ii) deliberately perform coarse\r\ngraining (for efficiency
reasons) has not been treated to its full extent. In this paper, we address the
pitfall associated with this situation, that the classical EDMD algorithm does
not automatically provide a Koopman operator approximation for the underlying
system if we do not carefully select the number of observables. Moreover, we show
that symmetries in the system dynamics can be carried over to the Koopman operator,
which allows us to massively increase the model efficiency. We also briefly draw
a connection to domain decomposition techniques for partial differential equations
and present numerical evidence using the Kuramoto--Sivashinsky equation."
author:
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
- first_name: Hans
full_name: Harder, Hans
id: '98879'
last_name: Harder
- first_name: Feliks
full_name: Nüske, Feliks
last_name: Nüske
- first_name: Friedrich
full_name: Philipp, Friedrich
last_name: Philipp
- first_name: Manuel
full_name: Schaller, Manuel
last_name: Schaller
- first_name: Karl
full_name: Worthmann, Karl
last_name: Worthmann
citation:
ama: Peitz S, Harder H, Nüske F, Philipp F, Schaller M, Worthmann K. Partial observations,
coarse graining and equivariance in Koopman operator theory for large-scale dynamical
systems. arXiv:230715325. Published online 2023.
apa: Peitz, S., Harder, H., Nüske, F., Philipp, F., Schaller, M., & Worthmann,
K. (2023). Partial observations, coarse graining and equivariance in Koopman
operator theory for large-scale dynamical systems. In arXiv:2307.15325.
bibtex: '@article{Peitz_Harder_Nüske_Philipp_Schaller_Worthmann_2023, title={Partial
observations, coarse graining and equivariance in Koopman operator theory for
large-scale dynamical systems}, journal={arXiv:2307.15325}, author={Peitz, Sebastian
and Harder, Hans and Nüske, Feliks and Philipp, Friedrich and Schaller, Manuel
and Worthmann, Karl}, year={2023} }'
chicago: Peitz, Sebastian, Hans Harder, Feliks Nüske, Friedrich Philipp, Manuel
Schaller, and Karl Worthmann. “Partial Observations, Coarse Graining and Equivariance
in Koopman Operator Theory for Large-Scale Dynamical Systems.” ArXiv:2307.15325,
2023.
ieee: S. Peitz, H. Harder, F. Nüske, F. Philipp, M. Schaller, and K. Worthmann,
“Partial observations, coarse graining and equivariance in Koopman operator theory
for large-scale dynamical systems,” arXiv:2307.15325. 2023.
mla: Peitz, Sebastian, et al. “Partial Observations, Coarse Graining and Equivariance
in Koopman Operator Theory for Large-Scale Dynamical Systems.” ArXiv:2307.15325,
2023.
short: S. Peitz, H. Harder, F. Nüske, F. Philipp, M. Schaller, K. Worthmann, ArXiv:2307.15325
(2023).
date_created: 2023-08-21T05:52:24Z
date_updated: 2023-08-21T05:53:35Z
department:
- _id: '655'
external_id:
arxiv:
- '2307.15325'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/pdf/2307.15325
oa: '1'
publication: arXiv:2307.15325
status: public
title: Partial observations, coarse graining and equivariance in Koopman operator
theory for large-scale dynamical systems
type: preprint
user_id: '47427'
year: '2023'
...
---
_id: '23428'
abstract:
- lang: eng
text: "The Koopman operator has become an essential tool for data-driven approximation
of dynamical (control) systems in recent years, e.g., via extended dynamic mode
decomposition. Despite its popularity, convergence results and, in particular,
error bounds are still quite scarce. In this paper, we derive probabilistic bounds
for the approximation error and the prediction error depending on the number of
training data points; for both ordinary and stochastic differential equations.
Moreover, we extend our analysis to nonlinear control-affine systems using either
ergodic trajectories or i.i.d.\r\nsamples. Here, we exploit the linearity of the
Koopman generator to obtain a bilinear system and, thus, circumvent the curse
of dimensionality since we do not autonomize the system by augmenting the state
by the control inputs. To the\r\nbest of our knowledge, this is the first finite-data
error analysis in the stochastic and/or control setting. Finally, we demonstrate
the effectiveness of the proposed approach by comparing it with state-of-the-art
techniques showing its superiority whenever state and control are coupled."
article_number: '14'
author:
- first_name: Feliks
full_name: Nüske, Feliks
id: '81513'
last_name: Nüske
orcid: 0000-0003-2444-7889
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
- first_name: Friedrich
full_name: Philipp, Friedrich
last_name: Philipp
- first_name: Manuel
full_name: Schaller, Manuel
last_name: Schaller
- first_name: Karl
full_name: Worthmann, Karl
last_name: Worthmann
citation:
ama: Nüske F, Peitz S, Philipp F, Schaller M, Worthmann K. Finite-data error bounds
for Koopman-based prediction and control. Journal of Nonlinear Science.
2023;33. doi:10.1007/s00332-022-09862-1
apa: Nüske, F., Peitz, S., Philipp, F., Schaller, M., & Worthmann, K. (2023).
Finite-data error bounds for Koopman-based prediction and control. Journal
of Nonlinear Science, 33, Article 14. https://doi.org/10.1007/s00332-022-09862-1
bibtex: '@article{Nüske_Peitz_Philipp_Schaller_Worthmann_2023, title={Finite-data
error bounds for Koopman-based prediction and control}, volume={33}, DOI={10.1007/s00332-022-09862-1},
number={14}, journal={Journal of Nonlinear Science}, author={Nüske, Feliks and
Peitz, Sebastian and Philipp, Friedrich and Schaller, Manuel and Worthmann, Karl},
year={2023} }'
chicago: Nüske, Feliks, Sebastian Peitz, Friedrich Philipp, Manuel Schaller, and
Karl Worthmann. “Finite-Data Error Bounds for Koopman-Based Prediction and Control.”
Journal of Nonlinear Science 33 (2023). https://doi.org/10.1007/s00332-022-09862-1.
ieee: 'F. Nüske, S. Peitz, F. Philipp, M. Schaller, and K. Worthmann, “Finite-data
error bounds for Koopman-based prediction and control,” Journal of Nonlinear
Science, vol. 33, Art. no. 14, 2023, doi: 10.1007/s00332-022-09862-1.'
mla: Nüske, Feliks, et al. “Finite-Data Error Bounds for Koopman-Based Prediction
and Control.” Journal of Nonlinear Science, vol. 33, 14, 2023, doi:10.1007/s00332-022-09862-1.
short: F. Nüske, S. Peitz, F. Philipp, M. Schaller, K. Worthmann, Journal of Nonlinear
Science 33 (2023).
date_created: 2021-08-17T12:25:09Z
date_updated: 2023-08-24T07:50:12Z
department:
- _id: '101'
- _id: '655'
doi: 10.1007/s00332-022-09862-1
intvolume: ' 33'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://link.springer.com/content/pdf/10.1007/s00332-022-09862-1.pdf
oa: '1'
publication: Journal of Nonlinear Science
publication_status: published
status: public
title: Finite-data error bounds for Koopman-based prediction and control
type: journal_article
user_id: '47427'
volume: 33
year: '2023'
...
---
_id: '21600'
abstract:
- lang: eng
text: Many problems in science and engineering require an efficient numerical approximation
of integrals or solutions to differential equations. For systems with rapidly
changing dynamics, an equidistant discretization is often inadvisable as it results
in prohibitively large errors or computational effort. To this end, adaptive schemes,
such as solvers based on Runge–Kutta pairs, have been developed which adapt the
step size based on local error estimations at each step. While the classical schemes
apply very generally and are highly efficient on regular systems, they can behave
suboptimally when an inefficient step rejection mechanism is triggered by structurally
complex systems such as chaotic systems. To overcome these issues, we propose
a method to tailor numerical schemes to the problem class at hand. This is achieved
by combining simple, classical quadrature rules or ODE solvers with data-driven
time-stepping controllers. Compared with learning solution operators to ODEs directly,
it generalizes better to unseen initial data as our approach employs classical
numerical schemes as base methods. At the same time it can make use of identified
structures of a problem class and, therefore, outperforms state-of-the-art adaptive
schemes. Several examples demonstrate superior efficiency. Source code is available
at https://github.com/lueckem/quadrature-ML.
author:
- first_name: Michael
full_name: Dellnitz, Michael
last_name: Dellnitz
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Marvin
full_name: Lücke, Marvin
last_name: Lücke
- first_name: Sina
full_name: Ober-Blöbaum, Sina
id: '16494'
last_name: Ober-Blöbaum
- first_name: Christian
full_name: Offen, Christian
id: '85279'
last_name: Offen
orcid: 0000-0002-5940-8057
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
- first_name: Karlson
full_name: Pfannschmidt, Karlson
id: '13472'
last_name: Pfannschmidt
orcid: 0000-0001-9407-7903
citation:
ama: Dellnitz M, Hüllermeier E, Lücke M, et al. Efficient time stepping for numerical
integration using reinforcement learning. SIAM Journal on Scientific Computing.
2023;45(2):A579-A595. doi:10.1137/21M1412682
apa: Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz,
S., & Pfannschmidt, K. (2023). Efficient time stepping for numerical integration
using reinforcement learning. SIAM Journal on Scientific Computing, 45(2),
A579–A595. https://doi.org/10.1137/21M1412682
bibtex: '@article{Dellnitz_Hüllermeier_Lücke_Ober-Blöbaum_Offen_Peitz_Pfannschmidt_2023,
title={Efficient time stepping for numerical integration using reinforcement
learning}, volume={45}, DOI={10.1137/21M1412682},
number={2}, journal={SIAM Journal on Scientific Computing}, author={Dellnitz,
Michael and Hüllermeier, Eyke and Lücke, Marvin and Ober-Blöbaum, Sina and Offen,
Christian and Peitz, Sebastian and Pfannschmidt, Karlson}, year={2023}, pages={A579–A595}
}'
chicago: 'Dellnitz, Michael, Eyke Hüllermeier, Marvin Lücke, Sina Ober-Blöbaum,
Christian Offen, Sebastian Peitz, and Karlson Pfannschmidt. “Efficient Time Stepping
for Numerical Integration Using Reinforcement Learning.” SIAM Journal on Scientific
Computing 45, no. 2 (2023): A579–95. https://doi.org/10.1137/21M1412682.'
ieee: 'M. Dellnitz et al., “Efficient time stepping for numerical integration
using reinforcement learning,” SIAM Journal on Scientific Computing, vol.
45, no. 2, pp. A579–A595, 2023, doi: 10.1137/21M1412682.'
mla: Dellnitz, Michael, et al. “Efficient Time Stepping for Numerical Integration
Using Reinforcement Learning.” SIAM Journal on Scientific Computing, vol.
45, no. 2, 2023, pp. A579–95, doi:10.1137/21M1412682.
short: M. Dellnitz, E. Hüllermeier, M. Lücke, S. Ober-Blöbaum, C. Offen, S. Peitz,
K. Pfannschmidt, SIAM Journal on Scientific Computing 45 (2023) A579–A595.
date_created: 2021-04-09T07:59:19Z
date_updated: 2023-08-25T09:24:50Z
ddc:
- '510'
department:
- _id: '101'
- _id: '636'
- _id: '355'
- _id: '655'
doi: 10.1137/21M1412682
external_id:
arxiv:
- arXiv:2104.03562
has_accepted_license: '1'
intvolume: ' 45'
issue: '2'
language:
- iso: eng
main_file_link:
- url: https://epubs.siam.org/doi/reader/10.1137/21M1412682
page: A579-A595
publication: SIAM Journal on Scientific Computing
publication_status: published
related_material:
link:
- description: GitHub
relation: software
url: https://github.com/lueckem/quadrature-ML
status: public
title: Efficient time stepping for numerical integration using reinforcement learning
type: journal_article
user_id: '47427'
volume: 45
year: '2023'
...
---
_id: '46739'
author:
- first_name: Somayeh
full_name: Sadeghi-Kohan, Somayeh
id: '78614'
last_name: Sadeghi-Kohan
orcid: https://orcid.org/0000-0001-7246-0610
- first_name: Sybille
full_name: Hellebrand, Sybille
id: '209'
last_name: Hellebrand
orcid: 0000-0002-3717-3939
- first_name: Hans-Joachim
full_name: Wunderlich, Hans-Joachim
last_name: Wunderlich
citation:
ama: 'Sadeghi-Kohan S, Hellebrand S, Wunderlich H-J. Low Power Streaming of Sensor
Data Using Gray Code-Based Approximate Communication. In: 2023 53rd Annual
IEEE/IFIP International Conference on Dependable Systems and Networks Workshops
(DSN-W). IEEE; 2023. doi:10.1109/dsn-w58399.2023.00056'
apa: Sadeghi-Kohan, S., Hellebrand, S., & Wunderlich, H.-J. (2023). Low Power
Streaming of Sensor Data Using Gray Code-Based Approximate Communication. 2023
53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks
Workshops (DSN-W). https://doi.org/10.1109/dsn-w58399.2023.00056
bibtex: '@inproceedings{Sadeghi-Kohan_Hellebrand_Wunderlich_2023, title={Low Power
Streaming of Sensor Data Using Gray Code-Based Approximate Communication}, DOI={10.1109/dsn-w58399.2023.00056},
booktitle={2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems
and Networks Workshops (DSN-W)}, publisher={IEEE}, author={Sadeghi-Kohan, Somayeh
and Hellebrand, Sybille and Wunderlich, Hans-Joachim}, year={2023} }'
chicago: Sadeghi-Kohan, Somayeh, Sybille Hellebrand, and Hans-Joachim Wunderlich.
“Low Power Streaming of Sensor Data Using Gray Code-Based Approximate Communication.”
In 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems
and Networks Workshops (DSN-W). IEEE, 2023. https://doi.org/10.1109/dsn-w58399.2023.00056.
ieee: 'S. Sadeghi-Kohan, S. Hellebrand, and H.-J. Wunderlich, “Low Power Streaming
of Sensor Data Using Gray Code-Based Approximate Communication,” 2023, doi: 10.1109/dsn-w58399.2023.00056.'
mla: Sadeghi-Kohan, Somayeh, et al. “Low Power Streaming of Sensor Data Using Gray
Code-Based Approximate Communication.” 2023 53rd Annual IEEE/IFIP International
Conference on Dependable Systems and Networks Workshops (DSN-W), IEEE, 2023,
doi:10.1109/dsn-w58399.2023.00056.
short: 'S. Sadeghi-Kohan, S. Hellebrand, H.-J. Wunderlich, in: 2023 53rd Annual
IEEE/IFIP International Conference on Dependable Systems and Networks Workshops
(DSN-W), IEEE, 2023.'
date_created: 2023-08-26T10:48:31Z
date_updated: 2023-08-26T10:49:07Z
department:
- _id: '48'
doi: 10.1109/dsn-w58399.2023.00056
language:
- iso: eng
publication: 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems
and Networks Workshops (DSN-W)
publication_status: published
publisher: IEEE
status: public
title: Low Power Streaming of Sensor Data Using Gray Code-Based Approximate Communication
type: conference
user_id: '78614'
year: '2023'
...