---
_id: '45191'
alternative_title:
- An Interdisciplinary Concept of Digital Working Environments in Industry 4.0
citation:
ama: Gräßler I, Maier GW, Steffen E, Roesmann D, eds. The Digital Twin of Humans.
Springer International Publishing; 2023. doi:10.1007/978-3-031-26104-6
apa: Gräßler, I., Maier, G. W., Steffen, E., & Roesmann, D. (Eds.). (2023).
The Digital Twin of Humans. Springer International Publishing. https://doi.org/10.1007/978-3-031-26104-6
bibtex: '@book{Gräßler_Maier_Steffen_Roesmann_2023, place={Cham}, title={The Digital
Twin of Humans}, DOI={10.1007/978-3-031-26104-6},
publisher={Springer International Publishing}, year={2023} }'
chicago: 'Gräßler, Iris, Günter W. Maier, Eckhard Steffen, and Daniel Roesmann,
eds. The Digital Twin of Humans. Cham: Springer International Publishing,
2023. https://doi.org/10.1007/978-3-031-26104-6.'
ieee: 'I. Gräßler, G. W. Maier, E. Steffen, and D. Roesmann, Eds., The Digital
Twin of Humans. Cham: Springer International Publishing, 2023.'
mla: Gräßler, Iris, et al., editors. The Digital Twin of Humans. Springer
International Publishing, 2023, doi:10.1007/978-3-031-26104-6.
short: I. Gräßler, G.W. Maier, E. Steffen, D. Roesmann, eds., The Digital Twin of
Humans, Springer International Publishing, Cham, 2023.
date_created: 2023-05-22T10:24:10Z
date_updated: 2024-03-25T09:07:55Z
department:
- _id: '542'
- _id: '152'
doi: 10.1007/978-3-031-26104-6
editor:
- first_name: Iris
full_name: Gräßler, Iris
id: '47565'
last_name: Gräßler
orcid: 0000-0001-5765-971X
- first_name: Günter W.
full_name: Maier, Günter W.
last_name: Maier
- first_name: Eckhard
full_name: Steffen, Eckhard
id: '15548'
last_name: Steffen
orcid: 0000-0002-9808-7401
- first_name: Daniel
full_name: Roesmann, Daniel
id: '54680'
last_name: Roesmann
language:
- iso: eng
place: Cham
publication_identifier:
isbn:
- '9783031261039'
- '9783031261046'
publication_status: published
publisher: Springer International Publishing
quality_controlled: '1'
status: public
title: The Digital Twin of Humans
type: book_editor
user_id: '5905'
year: '2023'
...
---
_id: '45971'
abstract:
- lang: eng
text: "Abstract\r\n An error estimate
for a canonical discretization of the harmonic map heat flow into spheres is derived.
The numerical scheme uses standard finite elements with a nodal treatment of linearized
unit-length constraints. The analysis is based on elementary approximation results
and only uses the discrete weak formulation."
author:
- first_name: Sören
full_name: Bartels, Sören
last_name: Bartels
- first_name: Balázs
full_name: Kovács, Balázs
id: '100441'
last_name: Kovács
orcid: 0000-0001-9872-3474
- first_name: Zhangxian
full_name: Wang, Zhangxian
last_name: Wang
citation:
ama: Bartels S, Kovács B, Wang Z. Error analysis for the numerical approximation
of the harmonic map heat flow with nodal constraints. IMA Journal of Numerical
Analysis. Published online 2023. doi:10.1093/imanum/drad037
apa: Bartels, S., Kovács, B., & Wang, Z. (2023). Error analysis for the numerical
approximation of the harmonic map heat flow with nodal constraints. IMA Journal
of Numerical Analysis. https://doi.org/10.1093/imanum/drad037
bibtex: '@article{Bartels_Kovács_Wang_2023, title={Error analysis for the numerical
approximation of the harmonic map heat flow with nodal constraints}, DOI={10.1093/imanum/drad037},
journal={IMA Journal of Numerical Analysis}, publisher={Oxford University Press
(OUP)}, author={Bartels, Sören and Kovács, Balázs and Wang, Zhangxian}, year={2023}
}'
chicago: Bartels, Sören, Balázs Kovács, and Zhangxian Wang. “Error Analysis for
the Numerical Approximation of the Harmonic Map Heat Flow with Nodal Constraints.”
IMA Journal of Numerical Analysis, 2023. https://doi.org/10.1093/imanum/drad037.
ieee: 'S. Bartels, B. Kovács, and Z. Wang, “Error analysis for the numerical approximation
of the harmonic map heat flow with nodal constraints,” IMA Journal of Numerical
Analysis, 2023, doi: 10.1093/imanum/drad037.'
mla: Bartels, Sören, et al. “Error Analysis for the Numerical Approximation of the
Harmonic Map Heat Flow with Nodal Constraints.” IMA Journal of Numerical Analysis,
Oxford University Press (OUP), 2023, doi:10.1093/imanum/drad037.
short: S. Bartels, B. Kovács, Z. Wang, IMA Journal of Numerical Analysis (2023).
date_created: 2023-07-10T12:32:10Z
date_updated: 2024-04-03T09:15:27Z
department:
- _id: '841'
doi: 10.1093/imanum/drad037
keyword:
- Applied Mathematics
- Computational Mathematics
- General Mathematics
language:
- iso: eng
publication: IMA Journal of Numerical Analysis
publication_identifier:
issn:
- 0272-4979
- 1464-3642
publication_status: published
publisher: Oxford University Press (OUP)
status: public
title: Error analysis for the numerical approximation of the harmonic map heat flow
with nodal constraints
type: journal_article
user_id: '100441'
year: '2023'
...
---
_id: '53140'
abstract:
- lang: eng
text: We present a new stability and error analysis of fully discrete approximation
schemes for the transient Stokes equation. For the spatial discretization, we
consider a wide class of Galerkin finite element methods which includes both inf-sup
stable spaces and symmetric pressure stabilized formulations. We extend the results
from Burman and Fernández [\textit{SIAM J. Numer. Anal.}, 47 (2009), pp. 409-439]
and provide a unified theoretical analysis of backward difference formulae (BDF
methods) of order 1 to 6. The main novelty of our approach lies in the use of
Dahlquist's G-stability concept together with multiplier techniques introduced
by Nevannlina-Odeh and recently by Akrivis et al. [\textit{SIAM J. Numer. Anal.},
59 (2021), pp. 2449-2472] to derive optimal stability and error estimates for
both the velocity and the pressure. When combined with a method dependent Ritz
projection for the initial data, unconditional stability can be shown while for
arbitrary interpolation, pressure stability is subordinate to the fulfillment
of a mild inverse CFL-type condition between space and time discretizations.
author:
- first_name: Alessandro
full_name: Contri, Alessandro
last_name: Contri
- first_name: Balázs
full_name: Kovács, Balázs
id: '100441'
last_name: Kovács
orcid: 0000-0001-9872-3474
- first_name: André
full_name: Massing, André
last_name: Massing
citation:
ama: 'Contri A, Kovács B, Massing A. Error analysis of BDF 1-6 time-stepping methods
for the transient Stokes problem: velocity and pressure estimates. arXiv.
Published online 2023. doi:10.48550/ARXIV.2312.05511'
apa: 'Contri, A., Kovács, B., & Massing, A. (2023). Error analysis of BDF 1-6
time-stepping methods for the transient Stokes problem: velocity and pressure
estimates. ArXiv. https://doi.org/10.48550/ARXIV.2312.05511'
bibtex: '@article{Contri_Kovács_Massing_2023, title={Error analysis of BDF 1-6 time-stepping
methods for the transient Stokes problem: velocity and pressure estimates}, DOI={10.48550/ARXIV.2312.05511},
journal={arXiv}, author={Contri, Alessandro and Kovács, Balázs and Massing, André},
year={2023} }'
chicago: 'Contri, Alessandro, Balázs Kovács, and André Massing. “Error Analysis
of BDF 1-6 Time-Stepping Methods for the Transient Stokes Problem: Velocity and
Pressure Estimates.” ArXiv, 2023. https://doi.org/10.48550/ARXIV.2312.05511.'
ieee: 'A. Contri, B. Kovács, and A. Massing, “Error analysis of BDF 1-6 time-stepping
methods for the transient Stokes problem: velocity and pressure estimates,” arXiv,
2023, doi: 10.48550/ARXIV.2312.05511.'
mla: 'Contri, Alessandro, et al. “Error Analysis of BDF 1-6 Time-Stepping Methods
for the Transient Stokes Problem: Velocity and Pressure Estimates.” ArXiv,
2023, doi:10.48550/ARXIV.2312.05511.'
short: A. Contri, B. Kovács, A. Massing, ArXiv (2023).
date_created: 2024-04-03T09:08:38Z
date_updated: 2024-04-03T09:12:47Z
department:
- _id: '841'
doi: 10.48550/ARXIV.2312.05511
language:
- iso: eng
publication: arXiv
status: public
title: 'Error analysis of BDF 1-6 time-stepping methods for the transient Stokes problem:
velocity and pressure estimates'
type: journal_article
user_id: '100441'
year: '2023'
...
---
_id: '53533'
author:
- first_name: Alena
full_name: Ernst, Alena
id: '46953'
last_name: Ernst
- first_name: Kai-Uwe
full_name: Schmidt, Kai-Uwe
last_name: Schmidt
citation:
ama: Ernst A, Schmidt K-U. Intersection theorems for finite general linear groups.
Mathematical Proceedings of the Cambridge Philosophical Society. 2023;175(1):129-160.
doi:10.1017/s0305004123000075
apa: Ernst, A., & Schmidt, K.-U. (2023). Intersection theorems for finite general
linear groups. Mathematical Proceedings of the Cambridge Philosophical Society,
175(1), 129–160. https://doi.org/10.1017/s0305004123000075
bibtex: '@article{Ernst_Schmidt_2023, title={Intersection theorems for finite general
linear groups}, volume={175}, DOI={10.1017/s0305004123000075},
number={1}, journal={Mathematical Proceedings of the Cambridge Philosophical Society},
publisher={Cambridge University Press (CUP)}, author={Ernst, Alena and Schmidt,
Kai-Uwe}, year={2023}, pages={129–160} }'
chicago: 'Ernst, Alena, and Kai-Uwe Schmidt. “Intersection Theorems for Finite General
Linear Groups.” Mathematical Proceedings of the Cambridge Philosophical Society
175, no. 1 (2023): 129–60. https://doi.org/10.1017/s0305004123000075.'
ieee: 'A. Ernst and K.-U. Schmidt, “Intersection theorems for finite general linear
groups,” Mathematical Proceedings of the Cambridge Philosophical Society,
vol. 175, no. 1, pp. 129–160, 2023, doi: 10.1017/s0305004123000075.'
mla: Ernst, Alena, and Kai-Uwe Schmidt. “Intersection Theorems for Finite General
Linear Groups.” Mathematical Proceedings of the Cambridge Philosophical Society,
vol. 175, no. 1, Cambridge University Press (CUP), 2023, pp. 129–60, doi:10.1017/s0305004123000075.
short: A. Ernst, K.-U. Schmidt, Mathematical Proceedings of the Cambridge Philosophical
Society 175 (2023) 129–160.
date_created: 2024-04-17T12:23:18Z
date_updated: 2024-05-07T08:29:59Z
department:
- _id: '100'
doi: 10.1017/s0305004123000075
intvolume: ' 175'
issue: '1'
keyword:
- General Mathematics
language:
- iso: eng
page: 129-160
publication: Mathematical Proceedings of the Cambridge Philosophical Society
publication_identifier:
issn:
- 0305-0041
- 1469-8064
publication_status: published
publisher: Cambridge University Press (CUP)
status: public
title: Intersection theorems for finite general linear groups
type: journal_article
user_id: '46953'
volume: 175
year: '2023'
...
---
_id: '55277'
author:
- first_name: B.
full_name: Klahn, B.
last_name: Klahn
- first_name: Marc
full_name: Technau, Marc
id: '106108'
last_name: Technau
orcid: 0000-0001-9650-2459
citation:
ama: Klahn B, Technau M. Galois groups of (n₀)+(n₁)X+…+(n₆)X6.
Int J Number Theory. 2023;19(10):2443–2450. doi:10.1142/S1793042123501208
apa: Klahn, B., & Technau, M. (2023). Galois groups of (n₀)+(n₁)X+…+(n₆)X6.
Int. J. Number Theory, 19(10), 2443–2450. https://doi.org/10.1142/S1793042123501208
bibtex: '@article{Klahn_Technau_2023, title={Galois groups of (n₀)+(n₁)X+…+(n₆)X6},
volume={19}, DOI={10.1142/S1793042123501208},
number={10}, journal={Int. J. Number Theory}, author={Klahn, B. and Technau, Marc},
year={2023}, pages={2443–2450} }'
chicago: 'Klahn, B., and Marc Technau. “Galois Groups of (n₀)+(n₁)X+…+(n₆)X6.”
Int. J. Number Theory 19, no. 10 (2023): 2443–2450. https://doi.org/10.1142/S1793042123501208.'
ieee: 'B. Klahn and M. Technau, “Galois groups of (n₀)+(n₁)X+…+(n₆)X6,”
Int. J. Number Theory, vol. 19, no. 10, pp. 2443–2450, 2023, doi: 10.1142/S1793042123501208.'
mla: Klahn, B., and Marc Technau. “Galois Groups of (n₀)+(n₁)X+…+(n₆)X6.”
Int. J. Number Theory, vol. 19, no. 10, 2023, pp. 2443–2450, doi:10.1142/S1793042123501208.
short: B. Klahn, M. Technau, Int. J. Number Theory 19 (2023) 2443–2450.
date_created: 2024-07-16T11:09:01Z
date_updated: 2024-07-24T07:23:33Z
department:
- _id: '102'
doi: 10.1142/S1793042123501208
extern: '1'
intvolume: ' 19'
issue: '10'
language:
- iso: eng
page: 2443–2450
publication: Int. J. Number Theory
status: public
title: Galois groups of (ⁿ₀)+(ⁿ₁)X+…+(ⁿ₆)X⁶
type: journal_article
user_id: '106108'
volume: 19
year: '2023'
...
---
_id: '55279'
author:
- first_name: P.
full_name: Minelli, P.
last_name: Minelli
- first_name: A.
full_name: Sourmelidis, A.
last_name: Sourmelidis
- first_name: Marc
full_name: Technau, Marc
id: '106108'
last_name: Technau
orcid: 0000-0001-9650-2459
citation:
ama: Minelli P, Sourmelidis A, Technau M. Bias in the number of steps in the Euclidean
algorithm and a conjecture of Ito on Dedekind sums. Math Ann. 2023;387:291–320.
doi:10.1007/s00208-022-02452-2
apa: Minelli, P., Sourmelidis, A., & Technau, M. (2023). Bias in the number
of steps in the Euclidean algorithm and a conjecture of Ito on Dedekind sums.
Math. Ann., 387, 291–320. https://doi.org/10.1007/s00208-022-02452-2
bibtex: '@article{Minelli_Sourmelidis_Technau_2023, title={Bias in the number of
steps in the Euclidean algorithm and a conjecture of Ito on Dedekind sums}, volume={387},
DOI={10.1007/s00208-022-02452-2},
journal={Math. Ann.}, author={Minelli, P. and Sourmelidis, A. and Technau, Marc},
year={2023}, pages={291–320} }'
chicago: 'Minelli, P., A. Sourmelidis, and Marc Technau. “Bias in the Number of
Steps in the Euclidean Algorithm and a Conjecture of Ito on Dedekind Sums.” Math.
Ann. 387 (2023): 291–320. https://doi.org/10.1007/s00208-022-02452-2.'
ieee: 'P. Minelli, A. Sourmelidis, and M. Technau, “Bias in the number of steps
in the Euclidean algorithm and a conjecture of Ito on Dedekind sums,” Math.
Ann., vol. 387, pp. 291–320, 2023, doi: 10.1007/s00208-022-02452-2.'
mla: Minelli, P., et al. “Bias in the Number of Steps in the Euclidean Algorithm
and a Conjecture of Ito on Dedekind Sums.” Math. Ann., vol. 387, 2023,
pp. 291–320, doi:10.1007/s00208-022-02452-2.
short: P. Minelli, A. Sourmelidis, M. Technau, Math. Ann. 387 (2023) 291–320.
date_created: 2024-07-16T11:09:01Z
date_updated: 2024-07-24T07:26:05Z
department:
- _id: '102'
doi: 10.1007/s00208-022-02452-2
extern: '1'
intvolume: ' 387'
language:
- iso: eng
page: 291–320
publication: Math. Ann.
status: public
title: Bias in the number of steps in the Euclidean algorithm and a conjecture of
Ito on Dedekind sums
type: journal_article
user_id: '106108'
volume: 387
year: '2023'
...
---
_id: '42163'
abstract:
- lang: eng
text: 'The article shows how to learn models of dynamical systems from data which
are governed by an unknown variational PDE. Rather than employing reduction techniques,
we learn a discrete field theory governed by a discrete Lagrangian density $L_d$
that is modelled as a neural network. Careful regularisation of the loss function
for training $L_d$ is necessary to obtain a field theory that is suitable for
numerical computations: we derive a regularisation term which optimises the solvability
of the discrete Euler--Lagrange equations. Secondly, we develop a method to find
solutions to machine learned discrete field theories which constitute travelling
waves of the underlying continuous PDE.'
author:
- 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
citation:
ama: 'Offen C, Ober-Blöbaum S. Learning discrete Lagrangians for variational PDEs
from data and detection of travelling waves. In: Nielsen F, Barbaresco F, eds.
Geometric Science of Information. Vol 14071. Lecture Notes in Computer
Science (LNCS). Springer, Cham.; 2023:569-579. doi:10.1007/978-3-031-38271-0_57'
apa: Offen, C., & Ober-Blöbaum, S. (2023). Learning discrete Lagrangians for
variational PDEs from data and detection of travelling waves. In F. Nielsen &
F. Barbaresco (Eds.), Geometric Science of Information (Vol. 14071, pp.
569–579). Springer, Cham. https://doi.org/10.1007/978-3-031-38271-0_57
bibtex: '@inproceedings{Offen_Ober-Blöbaum_2023, series={Lecture Notes in Computer
Science (LNCS)}, title={Learning discrete Lagrangians for variational PDEs from
data and detection of travelling waves}, volume={14071}, DOI={10.1007/978-3-031-38271-0_57},
booktitle={Geometric Science of Information}, publisher={Springer, Cham.}, author={Offen,
Christian and Ober-Blöbaum, Sina}, editor={Nielsen, F and Barbaresco, F}, year={2023},
pages={569–579}, collection={Lecture Notes in Computer Science (LNCS)} }'
chicago: Offen, Christian, and Sina Ober-Blöbaum. “Learning Discrete Lagrangians
for Variational PDEs from Data and Detection of Travelling Waves.” In Geometric
Science of Information, edited by F Nielsen and F Barbaresco, 14071:569–79.
Lecture Notes in Computer Science (LNCS). Springer, Cham., 2023. https://doi.org/10.1007/978-3-031-38271-0_57.
ieee: 'C. Offen and S. Ober-Blöbaum, “Learning discrete Lagrangians for variational
PDEs from data and detection of travelling waves,” in Geometric Science of
Information, Saint-Malo, Palais du Grand Large, France, 2023, vol. 14071,
pp. 569–579, doi: 10.1007/978-3-031-38271-0_57.'
mla: Offen, Christian, and Sina Ober-Blöbaum. “Learning Discrete Lagrangians for
Variational PDEs from Data and Detection of Travelling Waves.” Geometric Science
of Information, edited by F Nielsen and F Barbaresco, vol. 14071, Springer,
Cham., 2023, pp. 569–79, doi:10.1007/978-3-031-38271-0_57.
short: 'C. Offen, S. Ober-Blöbaum, in: F. Nielsen, F. Barbaresco (Eds.), Geometric
Science of Information, Springer, Cham., 2023, pp. 569–579.'
conference:
end_date: 2023-09-01
location: Saint-Malo, Palais du Grand Large, France
name: ' GSI''23 6th International Conference on Geometric Science of Information'
start_date: 2023-08-30
date_created: 2023-02-16T11:32:48Z
date_updated: 2024-08-12T13:46:29Z
ddc:
- '510'
department:
- _id: '636'
doi: 10.1007/978-3-031-38271-0_57
editor:
- first_name: F
full_name: Nielsen, F
last_name: Nielsen
- first_name: F
full_name: Barbaresco, F
last_name: Barbaresco
external_id:
arxiv:
- '2302.08232 '
file:
- access_level: open_access
content_type: application/pdf
creator: coffen
date_created: 2023-08-02T12:04:17Z
date_updated: 2023-08-02T12:04:17Z
description: |-
The article shows how to learn models of dynamical systems
from data which are governed by an unknown variational PDE. Rather
than employing reduction techniques, we learn a discrete field theory
governed by a discrete Lagrangian density Ld that is modelled as a neural network. Careful regularisation of the loss function for training Ld is
necessary to obtain a field theory that is suitable for numerical computations: we derive a regularisation term which optimises the solvability of
the discrete Euler–Lagrange equations. Secondly, we develop a method to
find solutions to machine learned discrete field theories which constitute
travelling waves of the underlying continuous PDE.
file_id: '46273'
file_name: LDensityLearning.pdf
file_size: 1938962
relation: main_file
title: Learning discrete Lagrangians for variational PDEs from data and detection
of travelling waves
file_date_updated: 2023-08-02T12:04:17Z
has_accepted_license: '1'
intvolume: ' 14071'
keyword:
- System identification
- discrete Lagrangians
- travelling waves
language:
- iso: eng
oa: '1'
page: 569-579
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: Geometric Science of Information
publication_identifier:
eisbn:
- 978-3-031-38271-0
publication_status: published
publisher: Springer, Cham.
quality_controlled: '1'
related_material:
link:
- description: GitHub
relation: software
url: https://github.com/Christian-Offen/LagrangianDensityML
series_title: Lecture Notes in Computer Science (LNCS)
status: public
title: Learning discrete Lagrangians for variational PDEs from data and detection
of travelling waves
type: conference
user_id: '85279'
volume: 14071
year: '2023'
...
---
_id: '27426'
abstract:
- lang: eng
text: "Regularization is used in many different areas of optimization when solutions\r\nare
sought which not only minimize a given function, but also possess a certain\r\ndegree
of regularity. Popular applications are image denoising, sparse\r\nregression
and machine learning. Since the choice of the regularization\r\nparameter is crucial
but often difficult, path-following methods are used to\r\napproximate the entire
regularization path, i.e., the set of all possible\r\nsolutions for all regularization
parameters. Due to their nature, the\r\ndevelopment of these methods requires
structural results about the\r\nregularization path. The goal of this article
is to derive these results for\r\nthe case of a smooth objective function which
is penalized by a piecewise\r\ndifferentiable regularization term. We do this
by treating regularization as a\r\nmultiobjective optimization problem. Our results
suggest that even in this\r\ngeneral case, the regularization path is piecewise
smooth. Moreover, our theory\r\nallows for a classification of the nonsmooth features
that occur in between\r\nsmooth parts. This is demonstrated in two applications,
namely support-vector\r\nmachines and exact penalty methods."
author:
- first_name: Bennet
full_name: Gebken, Bennet
id: '32643'
last_name: Gebken
- first_name: Katharina
full_name: Bieker, Katharina
id: '32829'
last_name: Bieker
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
citation:
ama: Gebken B, Bieker K, Peitz S. On the structure of regularization paths for piecewise
differentiable regularization terms. Journal of Global Optimization. 2023;85(3):709-741.
doi:10.1007/s10898-022-01223-2
apa: Gebken, B., Bieker, K., & Peitz, S. (2023). On the structure of regularization
paths for piecewise differentiable regularization terms. Journal of Global
Optimization, 85(3), 709–741. https://doi.org/10.1007/s10898-022-01223-2
bibtex: '@article{Gebken_Bieker_Peitz_2023, title={On the structure of regularization
paths for piecewise differentiable regularization terms}, volume={85}, DOI={10.1007/s10898-022-01223-2},
number={3}, journal={Journal of Global Optimization}, author={Gebken, Bennet and
Bieker, Katharina and Peitz, Sebastian}, year={2023}, pages={709–741} }'
chicago: 'Gebken, Bennet, Katharina Bieker, and Sebastian Peitz. “On the Structure
of Regularization Paths for Piecewise Differentiable Regularization Terms.” Journal
of Global Optimization 85, no. 3 (2023): 709–41. https://doi.org/10.1007/s10898-022-01223-2.'
ieee: 'B. Gebken, K. Bieker, and S. Peitz, “On the structure of regularization paths
for piecewise differentiable regularization terms,” Journal of Global Optimization,
vol. 85, no. 3, pp. 709–741, 2023, doi: 10.1007/s10898-022-01223-2.'
mla: Gebken, Bennet, et al. “On the Structure of Regularization Paths for Piecewise
Differentiable Regularization Terms.” Journal of Global Optimization, vol.
85, no. 3, 2023, pp. 709–41, doi:10.1007/s10898-022-01223-2.
short: B. Gebken, K. Bieker, S. Peitz, Journal of Global Optimization 85 (2023)
709–741.
date_created: 2021-11-15T09:24:59Z
date_updated: 2023-03-11T17:16:33Z
department:
- _id: '101'
- _id: '655'
doi: 10.1007/s10898-022-01223-2
intvolume: ' 85'
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://link.springer.com/content/pdf/10.1007/s10898-022-01223-2.pdf
oa: '1'
page: 709-741
publication: Journal of Global Optimization
status: public
title: On the structure of regularization paths for piecewise differentiable regularization
terms
type: journal_article
user_id: '47427'
volume: 85
year: '2023'
...
---
_id: '44857'
abstract:
- lang: eng
text: Ancestral reconstruction is a classic task in comparative genomics. Here,
we study the genome median problem, a related computational problem which, given
a set of three or more genomes, asks to find a new genome that minimizes the sum
of pairwise distances between it and the given genomes. The distance stands for
the amount of evolution observed at the genome level, for which we determine the
minimum number of rearrangement operations necessary to transform one genome into
the other. For almost all rearrangement operations the median problem is NP-hard,
with the exception of the breakpoint median that can be constructed efficiently
for multichromosomal circular and mixed genomes. In this work, we study the median
problem under a restricted rearrangement measure called c4-distance, which is
closely related to the breakpoint and the DCJ distance. We identify tight bounds
and decomposers of the c4-median and develop algorithms for its construction,
one exact ILP-based and three combinatorial heuristics. Subsequently, we perform
experiments on simulated data sets. Our results suggest that the c4-distance is
useful for the study the genome median problem, from theoretical and practical
perspectives.
author:
- first_name: Helmuth O.M.
full_name: Silva, Helmuth O.M.
last_name: Silva
- first_name: Diego P.
full_name: Rubert, Diego P.
last_name: Rubert
- first_name: Eloi
full_name: Araujo, Eloi
last_name: Araujo
- first_name: Eckhard
full_name: Steffen, Eckhard
id: '15548'
last_name: Steffen
orcid: 0000-0002-9808-7401
- first_name: Daniel
full_name: Doerr, Daniel
last_name: Doerr
- first_name: Fábio V.
full_name: Martinez, Fábio V.
last_name: Martinez
citation:
ama: Silva HOM, Rubert DP, Araujo E, Steffen E, Doerr D, Martinez FV. Algorithms
for the genome median under a restricted measure of rearrangement. RAIRO -
Operations Research. 2023;57(3):1045-1058. doi:10.1051/ro/2023052
apa: Silva, H. O. M., Rubert, D. P., Araujo, E., Steffen, E., Doerr, D., & Martinez,
F. V. (2023). Algorithms for the genome median under a restricted measure of rearrangement.
RAIRO - Operations Research, 57(3), 1045–1058. https://doi.org/10.1051/ro/2023052
bibtex: '@article{Silva_Rubert_Araujo_Steffen_Doerr_Martinez_2023, title={Algorithms
for the genome median under a restricted measure of rearrangement}, volume={57},
DOI={10.1051/ro/2023052}, number={3},
journal={RAIRO - Operations Research}, publisher={EDP Sciences}, author={Silva,
Helmuth O.M. and Rubert, Diego P. and Araujo, Eloi and Steffen, Eckhard and Doerr,
Daniel and Martinez, Fábio V.}, year={2023}, pages={1045–1058} }'
chicago: 'Silva, Helmuth O.M., Diego P. Rubert, Eloi Araujo, Eckhard Steffen, Daniel
Doerr, and Fábio V. Martinez. “Algorithms for the Genome Median under a Restricted
Measure of Rearrangement.” RAIRO - Operations Research 57, no. 3 (2023):
1045–58. https://doi.org/10.1051/ro/2023052.'
ieee: 'H. O. M. Silva, D. P. Rubert, E. Araujo, E. Steffen, D. Doerr, and F. V.
Martinez, “Algorithms for the genome median under a restricted measure of rearrangement,”
RAIRO - Operations Research, vol. 57, no. 3, pp. 1045–1058, 2023, doi:
10.1051/ro/2023052.'
mla: Silva, Helmuth O. M., et al. “Algorithms for the Genome Median under a Restricted
Measure of Rearrangement.” RAIRO - Operations Research, vol. 57, no. 3,
EDP Sciences, 2023, pp. 1045–58, doi:10.1051/ro/2023052.
short: H.O.M. Silva, D.P. Rubert, E. Araujo, E. Steffen, D. Doerr, F.V. Martinez,
RAIRO - Operations Research 57 (2023) 1045–1058.
date_created: 2023-05-16T08:48:22Z
date_updated: 2023-05-16T08:49:30Z
department:
- _id: '542'
doi: 10.1051/ro/2023052
intvolume: ' 57'
issue: '3'
keyword:
- Management Science and Operations Research
- Computer Science Applications
- Theoretical Computer Science
language:
- iso: eng
page: 1045-1058
publication: RAIRO - Operations Research
publication_identifier:
issn:
- 0399-0559
- 2804-7303
publication_status: published
publisher: EDP Sciences
status: public
title: Algorithms for the genome median under a restricted measure of rearrangement
type: journal_article
user_id: '15540'
volume: 57
year: '2023'
...
---
_id: '44859'
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. Sets of r-graphs that color all r-graphs.
arXiv:230508619. Published online 2023.
apa: Ma, Y., Mattiolo, D., Steffen, E., & Wolf, I. H. (2023). Sets of r-graphs
that color all r-graphs. In arXiv:2305.08619.
bibtex: '@article{Ma_Mattiolo_Steffen_Wolf_2023, title={Sets of r-graphs that color
all r-graphs}, journal={arXiv:2305.08619}, author={Ma, Yulai and Mattiolo, Davide
and Steffen, Eckhard and Wolf, Isaak Hieronymus}, year={2023} }'
chicago: Ma, Yulai, Davide Mattiolo, Eckhard Steffen, and Isaak Hieronymus Wolf.
“Sets of R-Graphs That Color All r-Graphs.” ArXiv:2305.08619, 2023.
ieee: Y. Ma, D. Mattiolo, E. Steffen, and I. H. Wolf, “Sets of r-graphs that color
all r-graphs,” arXiv:2305.08619. 2023.
mla: Ma, Yulai, et al. “Sets of R-Graphs That Color All r-Graphs.” ArXiv:2305.08619,
2023.
short: Y. Ma, D. Mattiolo, E. Steffen, I.H. Wolf, ArXiv:2305.08619 (2023).
date_created: 2023-05-16T10:07:47Z
date_updated: 2023-05-16T11:17:26Z
department:
- _id: '542'
external_id:
arxiv:
- '2305.08619'
language:
- iso: eng
publication: arXiv:2305.08619
status: public
title: Sets of r-graphs that color all r-graphs
type: preprint
user_id: '15540'
year: '2023'
...
---
_id: '45498'
abstract:
- lang: eng
text: "We present a novel method for high-order phase reduction in networks of\r\nweakly
coupled oscillators and, more generally, perturbations of reducible\r\nnormally
hyperbolic (quasi-)periodic tori. Our method works by computing an\r\nasymptotic
expansion for an embedding of the perturbed invariant torus, as well\r\nas for
the reduced phase dynamics in local coordinates. Both can be determined\r\nto
arbitrary degrees of accuracy, and we show that the phase dynamics may\r\ndirectly
be obtained in normal form. We apply the method to predict remote\r\nsynchronisation
in a chain of coupled Stuart-Landau oscillators."
author:
- first_name: Sören
full_name: von der Gracht, Sören
id: '97359'
last_name: von der Gracht
orcid: 0000-0002-8054-2058
- first_name: Eddie
full_name: Nijholt, Eddie
last_name: Nijholt
- first_name: Bob
full_name: Rink, Bob
last_name: Rink
citation:
ama: von der Gracht S, Nijholt E, Rink B. A parametrisation method for high-order
phase reduction in coupled oscillator networks. arXiv:230603320.
apa: von der Gracht, S., Nijholt, E., & Rink, B. (n.d.). A parametrisation method
for high-order phase reduction in coupled oscillator networks. In arXiv:2306.03320.
bibtex: '@article{von der Gracht_Nijholt_Rink, title={A parametrisation method for
high-order phase reduction in coupled oscillator networks}, journal={arXiv:2306.03320},
author={von der Gracht, Sören and Nijholt, Eddie and Rink, Bob} }'
chicago: Gracht, Sören von der, Eddie Nijholt, and Bob Rink. “A Parametrisation
Method for High-Order Phase Reduction in Coupled Oscillator Networks.” ArXiv:2306.03320,
n.d.
ieee: S. von der Gracht, E. Nijholt, and B. Rink, “A parametrisation method for
high-order phase reduction in coupled oscillator networks,” arXiv:2306.03320.
.
mla: von der Gracht, Sören, et al. “A Parametrisation Method for High-Order Phase
Reduction in Coupled Oscillator Networks.” ArXiv:2306.03320.
short: S. von der Gracht, E. Nijholt, B. Rink, ArXiv:2306.03320 (n.d.).
date_created: 2023-06-07T07:57:28Z
date_updated: 2023-06-07T07:59:06Z
department:
- _id: '101'
external_id:
arxiv:
- '2306.03320'
language:
- iso: eng
main_file_link:
- url: https://arxiv.org/pdf/2306.03320
page: '29'
publication: arXiv:2306.03320
publication_status: submitted
status: public
title: A parametrisation method for high-order phase reduction in coupled oscillator
networks
type: preprint
user_id: '97359'
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: '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: '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: '16296'
abstract:
- lang: eng
text: "Multiobjective optimization plays an increasingly important role in modern\r\napplications,
where several objectives are often of equal importance. The task\r\nin multiobjective
optimization and multiobjective optimal control is therefore\r\nto compute the
set of optimal compromises (the Pareto set) between the\r\nconflicting objectives.
Since the Pareto set generally consists of an infinite\r\nnumber of solutions,
the computational effort can quickly become challenging\r\nwhich is particularly
problematic when the objectives are costly to evaluate as\r\nis the case for models
governed by partial differential equations (PDEs). To\r\ndecrease the numerical
effort to an affordable amount, surrogate models can be\r\nused to replace the
expensive PDE evaluations. Existing multiobjective\r\noptimization methods using
model reduction are limited either to low parameter\r\ndimensions or to few (ideally
two) objectives. In this article, we present a\r\ncombination of the reduced basis
model reduction method with a continuation\r\napproach using inexact gradients.
The resulting approach can handle an\r\narbitrary number of objectives while yielding
a significant reduction in\r\ncomputing time."
author:
- first_name: Stefan
full_name: Banholzer, Stefan
last_name: Banholzer
- first_name: Bennet
full_name: Gebken, Bennet
id: '32643'
last_name: Gebken
- first_name: Michael
full_name: Dellnitz, Michael
last_name: Dellnitz
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: https://orcid.org/0000-0002-3389-793X
- first_name: Stefan
full_name: Volkwein, Stefan
last_name: Volkwein
citation:
ama: 'Banholzer S, Gebken B, Dellnitz M, Peitz S, Volkwein S. ROM-Based Multiobjective
Optimization of Elliptic PDEs via Numerical Continuation. In: Michael H, Roland
H, Christian K, Michael U, Stefan U, eds. Non-Smooth and Complementarity-Based
Distributed Parameter Systems. Springer; 2022:43-76. doi:10.1007/978-3-030-79393-7_3'
apa: Banholzer, S., Gebken, B., Dellnitz, M., Peitz, S., & Volkwein, S. (2022).
ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation.
In H. Michael, H. Roland, K. Christian, U. Michael, & U. Stefan (Eds.), Non-Smooth
and Complementarity-Based Distributed Parameter Systems (pp. 43–76). Springer.
https://doi.org/10.1007/978-3-030-79393-7_3
bibtex: '@inbook{Banholzer_Gebken_Dellnitz_Peitz_Volkwein_2022, place={Cham}, title={ROM-Based
Multiobjective Optimization of Elliptic PDEs via Numerical Continuation}, DOI={10.1007/978-3-030-79393-7_3},
booktitle={Non-Smooth and Complementarity-Based Distributed Parameter Systems},
publisher={Springer}, author={Banholzer, Stefan and Gebken, Bennet and Dellnitz,
Michael and Peitz, Sebastian and Volkwein, Stefan}, editor={Michael, Hintermüller
and Roland, Herzog and Christian, Kanzow and Michael, Ulbrich and Stefan, Ulbrich},
year={2022}, pages={43–76} }'
chicago: 'Banholzer, Stefan, Bennet Gebken, Michael Dellnitz, Sebastian Peitz, and
Stefan Volkwein. “ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical
Continuation.” In Non-Smooth and Complementarity-Based Distributed Parameter
Systems, edited by Hintermüller Michael, Herzog Roland, Kanzow Christian,
Ulbrich Michael, and Ulbrich Stefan, 43–76. Cham: Springer, 2022. https://doi.org/10.1007/978-3-030-79393-7_3.'
ieee: 'S. Banholzer, B. Gebken, M. Dellnitz, S. Peitz, and S. Volkwein, “ROM-Based
Multiobjective Optimization of Elliptic PDEs via Numerical Continuation,” in Non-Smooth
and Complementarity-Based Distributed Parameter Systems, H. Michael, H. Roland,
K. Christian, U. Michael, and U. Stefan, Eds. Cham: Springer, 2022, pp. 43–76.'
mla: Banholzer, Stefan, et al. “ROM-Based Multiobjective Optimization of Elliptic
PDEs via Numerical Continuation.” Non-Smooth and Complementarity-Based Distributed
Parameter Systems, edited by Hintermüller Michael et al., Springer, 2022,
pp. 43–76, doi:10.1007/978-3-030-79393-7_3.
short: 'S. Banholzer, B. Gebken, M. Dellnitz, S. Peitz, S. Volkwein, in: H. Michael,
H. Roland, K. Christian, U. Michael, U. Stefan (Eds.), Non-Smooth and Complementarity-Based
Distributed Parameter Systems, Springer, Cham, 2022, pp. 43–76.'
date_created: 2020-03-13T12:45:31Z
date_updated: 2022-03-14T13:04:51Z
department:
- _id: '101'
- _id: '655'
doi: 10.1007/978-3-030-79393-7_3
editor:
- first_name: Hintermüller
full_name: Michael, Hintermüller
last_name: Michael
- first_name: Herzog
full_name: Roland, Herzog
last_name: Roland
- first_name: Kanzow
full_name: Christian, Kanzow
last_name: Christian
- first_name: Ulbrich
full_name: Michael, Ulbrich
last_name: Michael
- first_name: Ulbrich
full_name: Stefan, Ulbrich
last_name: Stefan
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/pdf/1906.09075.pdf
oa: '1'
page: 43-76
place: Cham
publication: Non-Smooth and Complementarity-Based Distributed Parameter Systems
publication_identifier:
isbn:
- 978-3-030-79392-0
publisher: Springer
status: public
title: ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation
type: book_chapter
user_id: '47427'
year: '2022'
...
---
_id: '30294'
abstract:
- lang: eng
text: With the ever increasing capabilities of sensors and controllers, autonomous
driving is quickly becoming a reality. This disruptive change in the automotive
industry poses major challenges for manufacturers as well as suppliers as entirely
new design and testing strategies have to be developed to remain competitive.
Most importantly, the complexity of autonomously driving vehicles in a complex,
uncertain, and safety-critical environment requires new testing procedures to
cover the almost infinite range of potential scenarios.
author:
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
- first_name: Michael
full_name: Dellnitz, Michael
last_name: Dellnitz
- first_name: Sebastian
full_name: Bannenberg, Sebastian
last_name: Bannenberg
citation:
ama: 'Peitz S, Dellnitz M, Bannenberg S. Efficient Virtual Design and Testing of
Autonomous Vehicles. In: Bock HG, Küfer K-H, Maas P, Milde A, Schulz V, eds. German
Success Stories in Industrial Mathematics. Vol 35. Mathematics in Industry.
Springer International Publishing; 2022. doi:10.1007/978-3-030-81455-7_23'
apa: Peitz, S., Dellnitz, M., & Bannenberg, S. (2022). Efficient Virtual Design
and Testing of Autonomous Vehicles. In H. G. Bock, K.-H. Küfer, P. Maas, A. Milde,
& V. Schulz (Eds.), German Success Stories in Industrial Mathematics
(Vol. 35). Springer International Publishing. https://doi.org/10.1007/978-3-030-81455-7_23
bibtex: '@inbook{Peitz_Dellnitz_Bannenberg_2022, place={Cham}, series={Mathematics
in Industry}, title={Efficient Virtual Design and Testing of Autonomous Vehicles},
volume={35}, DOI={10.1007/978-3-030-81455-7_23},
booktitle={German Success Stories in Industrial Mathematics}, publisher={Springer
International Publishing}, author={Peitz, Sebastian and Dellnitz, Michael and
Bannenberg, Sebastian}, editor={Bock, H. G. and Küfer, K.-H. and Maas, P. and
Milde, A. and Schulz, V.}, year={2022}, collection={Mathematics in Industry} }'
chicago: 'Peitz, Sebastian, Michael Dellnitz, and Sebastian Bannenberg. “Efficient
Virtual Design and Testing of Autonomous Vehicles.” In German Success Stories
in Industrial Mathematics, edited by H. G. Bock, K.-H. Küfer, P. Maas, A.
Milde, and V. Schulz, Vol. 35. Mathematics in Industry. Cham: Springer International
Publishing, 2022. https://doi.org/10.1007/978-3-030-81455-7_23.'
ieee: 'S. Peitz, M. Dellnitz, and S. Bannenberg, “Efficient Virtual Design and Testing
of Autonomous Vehicles,” in German Success Stories in Industrial Mathematics,
vol. 35, H. G. Bock, K.-H. Küfer, P. Maas, A. Milde, and V. Schulz, Eds. Cham:
Springer International Publishing, 2022.'
mla: Peitz, Sebastian, et al. “Efficient Virtual Design and Testing of Autonomous
Vehicles.” German Success Stories in Industrial Mathematics, edited by
H. G. Bock et al., vol. 35, Springer International Publishing, 2022, doi:10.1007/978-3-030-81455-7_23.
short: 'S. Peitz, M. Dellnitz, S. Bannenberg, in: H.G. Bock, K.-H. Küfer, P. Maas,
A. Milde, V. Schulz (Eds.), German Success Stories in Industrial Mathematics,
Springer International Publishing, Cham, 2022.'
date_created: 2022-03-14T07:32:41Z
date_updated: 2022-03-14T07:42:01Z
department:
- _id: '101'
- _id: '655'
doi: 10.1007/978-3-030-81455-7_23
editor:
- first_name: H. G.
full_name: Bock, H. G.
last_name: Bock
- first_name: K.-H.
full_name: Küfer, K.-H.
last_name: Küfer
- first_name: P.
full_name: Maas, P.
last_name: Maas
- first_name: A.
full_name: Milde, A.
last_name: Milde
- first_name: V.
full_name: Schulz, V.
last_name: Schulz
intvolume: ' 35'
language:
- iso: eng
place: Cham
publication: German Success Stories in Industrial Mathematics
publication_identifier:
isbn:
- '9783030814540'
- '9783030814557'
issn:
- 1612-3956
- 2198-3283
publication_status: published
publisher: Springer International Publishing
series_title: Mathematics in Industry
status: public
title: Efficient Virtual Design and Testing of Autonomous Vehicles
type: book_chapter
user_id: '47427'
volume: 35
year: '2022'
...
---
_id: '30490'
author:
- first_name: Jacky
full_name: Cresson, Jacky
last_name: Cresson
- first_name: Fernando
full_name: Jiménez, Fernando
last_name: Jiménez
- first_name: Sina
full_name: Ober-Blöbaum, Sina
id: '16494'
last_name: Ober-Blöbaum
citation:
ama: Cresson J, Jiménez F, Ober-Blöbaum S. Continuous and discrete Noether’s fractional
conserved quantities for restricted calculus of variations. AIMS. 2022;14(1):57-89.
apa: Cresson, J., Jiménez, F., & Ober-Blöbaum, S. (2022). Continuous and discrete
Noether’s fractional conserved quantities for restricted calculus of variations.
AIMS, 14(1), 57–89.
bibtex: '@article{Cresson_Jiménez_Ober-Blöbaum_2022, title={Continuous and discrete
Noether’s fractional conserved quantities for restricted calculus of variations},
volume={14(1)}, journal={AIMS}, author={Cresson, Jacky and Jiménez, Fernando and
Ober-Blöbaum, Sina}, year={2022}, pages={57–89} }'
chicago: 'Cresson, Jacky, Fernando Jiménez, and Sina Ober-Blöbaum. “Continuous and
Discrete Noether’s Fractional Conserved Quantities for Restricted Calculus of
Variations.” AIMS 14(1) (2022): 57–89.'
ieee: J. Cresson, F. Jiménez, and S. Ober-Blöbaum, “Continuous and discrete Noether’s
fractional conserved quantities for restricted calculus of variations,” AIMS,
vol. 14(1), pp. 57–89, 2022.
mla: Cresson, Jacky, et al. “Continuous and Discrete Noether’s Fractional Conserved
Quantities for Restricted Calculus of Variations.” AIMS, vol. 14(1), 2022,
pp. 57–89.
short: J. Cresson, F. Jiménez, S. Ober-Blöbaum, AIMS 14(1) (2022) 57–89.
date_created: 2022-03-24T12:26:10Z
date_updated: 2022-03-24T12:26:32Z
department:
- _id: '636'
language:
- iso: eng
page: 57-89
publication: AIMS
status: public
title: Continuous and discrete Noether's fractional conserved quantities for restricted
calculus of variations
type: journal_article
user_id: '15694'
volume: 14(1)
year: '2022'
...