@article{45816, author = {{Dieter, Peter and Stumpe, Miriam and Ulmer, Marlin Wolf and Schryen, Guido}}, journal = {{Transportation Research Part D}}, title = {{{Anticipatory Assignment of Passengers to Meeting Points for Taxi-Ridesharing}}}, volume = {{121}}, year = {{2023}}, } @techreport{46102, author = {{Leineweber, Jonas}}, publisher = {{H-Soz-Kult}}, title = {{{Tagungsbericht: 10/20 Jahre UNESCO-Konvention zum Erhalt des Immateriellen Kulturerbes – Auftaktveranstaltung zum Doppeljubiläum}}}, year = {{2023}}, } @inbook{46154, author = {{Janzen, Thomas and Gabel, Stephan and Gampert, Martha and Matz, Frauke and Reckermann, Julia}}, booktitle = {{Digitalisierung in der Hochschullehre – Perspektiven und Gestaltungsoptionen}}, editor = {{Mrohs, Lorenz and Hess, Miriam and Lindner, Konstantin and Schlüter, Julia and Overhage, Sven}}, pages = {{151--154}}, publisher = {{University of Bamberg Press}}, title = {{{Das DigitELE Tutorial: Eine digitale Lernumgebung in der Englischdidaktik}}}, year = {{2023}}, } @article{38041, abstract = {{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. 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 = {{Meyer, Marius and Kenter, Tobias and Plessl, Christian}}, issn = {{1936-7406}}, journal = {{ACM Transactions on Reconfigurable Technology and Systems}}, keywords = {{General Computer Science}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{Multi-FPGA Designs and Scaling of HPC Challenge Benchmarks via MPI and Circuit-Switched Inter-FPGA Networks}}}, doi = {{10.1145/3576200}}, year = {{2023}}, } @article{46243, author = {{Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, journal = {{ECML-PKDD}}, location = {{Torino}}, title = {{{Clifford Embeddings – A Generalized Approach for Embedding in Normed Algebras}}}, year = {{2023}}, } @article{46251, author = {{Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, journal = {{International Joint Conference on Artificial Intelligence}}, location = {{Macau}}, title = {{{Neuro-Symbolic Class Expression Learning}}}, year = {{2023}}, } @article{45361, abstract = {{ 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. }}, author = {{Schade, Robert and Kenter, Tobias and Elgabarty, Hossam and Lass, Michael and Kühne, Thomas and Plessl, Christian}}, issn = {{1094-3420}}, journal = {{The International Journal of High Performance Computing Applications}}, keywords = {{Hardware and Architecture, Theoretical Computer Science, Software}}, publisher = {{SAGE Publications}}, title = {{{Breaking the exascale barrier for the electronic structure problem in ab-initio molecular dynamics}}}, doi = {{10.1177/10943420231177631}}, year = {{2023}}, } @phdthesis{45780, author = {{Tornede, Alexander}}, 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 }}, year = {{2023}}, } @article{29240, abstract = {{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, 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.}}, author = {{Ober-Blöbaum, Sina and Offen, Christian}}, issn = {{0377-0427}}, journal = {{Journal of Computational and Applied Mathematics}}, keywords = {{Lagrangian learning, variational backward error analysis, modified Lagrangian, variational integrators, physics informed learning}}, pages = {{114780}}, publisher = {{Elsevier}}, title = {{{Variational Learning of Euler–Lagrange Dynamics from Data}}}, doi = {{10.1016/j.cam.2022.114780}}, volume = {{421}}, year = {{2023}}, } @article{29236, abstract = {{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.}}, author = {{McLachlan, Robert and Offen, Christian}}, journal = {{Journal of Geometric Mechanics}}, keywords = {{variational integrators, backward error analysis, Euler--Lagrange equations, multistep methods, conjugate symplectic methods}}, number = {{1}}, pages = {{98--115}}, publisher = {{AIMS Press}}, title = {{{Backward error analysis for conjugate symplectic methods}}}, doi = {{10.3934/jgm.2023005}}, volume = {{15}}, year = {{2023}}, } @article{37654, abstract = {{Recently, Hamiltonian neural networks (HNN) have been introduced to incorporate prior physical knowledge when learning the dynamical equations of Hamiltonian systems. Hereby, the symplectic system structure is preserved despite the data-driven modeling approach. However, preserving symmetries requires additional attention. In this research, we enhance the HNN with a Lie algebra framework to detect and embed symmetries in the neural network. This approach allows to simultaneously learn the symmetry group action and the total energy of the system. As illustrating examples, a pendulum on a cart and a two-body problem from astrodynamics are considered.}}, author = {{Dierkes, Eva and Offen, Christian and Ober-Blöbaum, Sina and Flaßkamp, Kathrin}}, issn = {{1054-1500}}, journal = {{Chaos}}, number = {{6}}, publisher = {{AIP Publishing}}, title = {{{Hamiltonian Neural Networks with Automatic Symmetry Detection}}}, doi = {{10.1063/5.0142969}}, volume = {{33}}, year = {{2023}}, } @phdthesis{46482, abstract = {{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. With 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. To 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.}}, author = {{Sprenger, Alexander}}, keywords = {{Testantwortkompaktierung, Prozessvariation, Silicon Lifecycle Management}}, pages = {{xi, 160}}, publisher = {{Universität Paderborn}}, title = {{{Testinstrumente und Testdatenanalyse zur Verarbeitung von Unsicherheiten in Logikblöcken hochintegrierter Schaltungen}}}, doi = {{10.17619/UNIPB/1-1787}}, year = {{2023}}, } @unpublished{46579, abstract = {{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 dynamics 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 graining (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 = {{Peitz, Sebastian and Harder, Hans and Nüske, Feliks and Philipp, Friedrich and Schaller, Manuel and Worthmann, Karl}}, booktitle = {{arXiv:2307.15325}}, title = {{{Partial observations, coarse graining and equivariance in Koopman operator theory for large-scale dynamical systems}}}, year = {{2023}}, } @article{45602, author = {{Karakaya, Yasin and Somnitz, H. and Hermsen, A. and Gonchikzhapov, Munko and Kasper, Tina}}, issn = {{2666-352X}}, journal = {{Applications in Energy and Combustion Science}}, keywords = {{Metastable particles, Nanomaterials, Quartz-crystal-microbalance, Molecular-beam mass spectrometry, Tetramethylsilane, Reaction kinetics}}, publisher = {{Elsevier BV}}, title = {{{Revisiting the initial reaction rates for TMS combustion and a new evidence for metastable silica nanoparticles in the gas-phase synthesis}}}, doi = {{10.1016/j.jaecs.2023.100152}}, volume = {{14}}, year = {{2023}}, } @article{23428, abstract = {{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. samples. 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 best 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.}}, author = {{Nüske, Feliks and Peitz, Sebastian and Philipp, Friedrich and Schaller, Manuel and Worthmann, Karl}}, journal = {{Journal of Nonlinear Science}}, title = {{{Finite-data error bounds for Koopman-based prediction and control}}}, doi = {{10.1007/s00332-022-09862-1}}, volume = {{33}}, year = {{2023}}, } @article{46637, author = {{Gonchikzhapov, Munko and Kasper, Tina}}, issn = {{2666-352X}}, journal = {{Applications in Energy and Combustion Science}}, keywords = {{Nanoparticle synthesis, Flame spray pyrolysis, SpraySyn burner, Flame structure, Species distribution, Temperature distribution}}, publisher = {{Elsevier BV}}, title = {{{Thermal and chemical structure of ethanol and 2-ethylhexanoic acid/ethanol SpraySyn flames}}}, doi = {{10.1016/j.jaecs.2023.100174}}, volume = {{15}}, year = {{2023}}, } @article{46784, author = {{Wallscheid, Oliver and Peitz, Sebastian and Stenner, Jan and Weber, Daniel and Boshoff, Septimus and Meyer, Marvin and Chidananda, Vikas and Schweins, Oliver}}, issn = {{2475-9066}}, journal = {{Journal of Open Source Software}}, keywords = {{General Earth and Planetary Sciences, General Environmental Science}}, number = {{89}}, publisher = {{The Open Journal}}, title = {{{ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids}}}, doi = {{10.21105/joss.05616}}, volume = {{8}}, year = {{2023}}, } @article{46186, author = {{Höper, Lukas and Schulte, Carsten}}, issn = {{0025-5866}}, journal = {{MNU journal}}, number = {{4}}, pages = {{314--320}}, publisher = {{Verlag Klaus Seeberger}}, title = {{{Paradigmenwechsel vom klassischen zum datengetriebenen Problemlösen im Informatikunterricht}}}, volume = {{76}}, year = {{2023}}, } @inproceedings{35014, author = {{Blömer, Johannes and Bobolz, Jan and Bröcher, Henrik}}, location = {{Taipeh, Taiwan}}, title = {{{On the impossibility of surviving (iterated) deletion of weakly dominated strategies in rational MPC}}}, year = {{2023}}, } @inproceedings{43458, author = {{Blömer, Johannes and Bobolz, Jan and Porzenheim, Laurens Alexander}}, location = {{Guangzhou, China}}, title = {{{A Generic Construction of an Anonymous Reputation System and Instantiations from Lattices}}}, year = {{2023}}, }