Publications of Peter Benner

Working Paper (28)

2022
Working Paper
Benner, P.; Gugercin, S.; Werner, S. W. R.: A Unifying Framework for Tangential Interpolation of Structured Bilinear Control Systems. (2022)
Working Paper
Benner, P.; Nakatsukasa, Y.; Penke, C.: A Structure-Preserving Divide-and-Conquer Method for Pseudosymmetric Matrices. (2022)
Working Paper
Goyal, P. K.; Benner, P.: Neural ODEs with Irregular and Noisy Data. (2022)
Working Paper
Khorrami, M. S.; Mianroodi, J. R.; Siboni, N. H.; Goyal, P. K.; Svendsen, B.; Benner, P.; Raabe, D.: An Artificial Neural Network for Surrogate Modeling of Stress Fields in Viscoplastic Polycrystalline Materials. (2022)
Working Paper
Kumar, V.; Heiland, J.; Benner, P.: Exponential Lag Synchronization of Cohen-Grossberg Neural Networks with Discrete and Distributed Delays on Time Scales. (2022)
Working Paper
Przybilla, J.; Pontes Duff, I.; Benner, P.: Model Reduction for Second-Order Systems with Inhomogeneous Initial Conditions. (2022)
2021
Working Paper
Chellappa, S.; Feng, L.; de la Rubia, V.; Benner, P.: Inf-Sup-Constant-Free State Error Estimator for Model Order Reduction of Parametric Systems in Electromagnetics. (2021)
Working Paper
Goyal, P. K.; Benner, P.: LQResNet: A Deep Neural Network Architecture for Learning Dynamic Processes. (2021)
Working Paper
Goyal, P. K.; Benner, P.: Learning Dynamics from Noisy Measurements using Deep Learning with a Runge-Kutta Constraint. The Symbiosis of Deep Learning and Differential Equations Workshop at NeurIPS 2021, 9 pages (2021)
Working Paper
Goyal, P. K.; Benner, P.: Learning Low-Dimensional Quadratic-Embeddings of High-Fidelity Nonlinear Dynamics using Deep Learning. (2021)
Working Paper
Himpe, C.; Grundel, S.; Benner, P.: Next-Gen Gas Network Simulation. (2021)
Working Paper
Khattak, M. A.; Ahmad, M. I.; Feng, L.; Benner, P.: Multivariate Moment Matching for Model Order Reduction of Quadratic-Bilinear Systems using Error Bounds. (2021)
Working Paper
Kweyu, C. M.; Khoromskaia, V.; Khoromskij, B.; Stein, M.; Benner, P.: Solution Decomposition for the Nonlinear Poisson-Boltzmann Equation using the Range-Separated Tensor Format. (2021)
Working Paper
Sarna, N.; Benner, P.: Learning Reduced Order Models from Data for Hyperbolic PDEs. (2021)
2020
Working Paper
Banagaaya, N.; Ali, G.; Grundel, S.; Benner, P.: Automatic Decoupling and Index-aware Model-Order Reduction for Nonlinear Differential-Algebraic Equations. (2020)
Working Paper
Beddig, R. S.; Benner, P.; Dorschky, I.; Reis, T.; Schwerdtner, P.; Voigt, M.; Werner, S. W. R.: Structure-Preserving Model Reduction for Dissipative Mechanical Systems. (2020)
Working Paper
Benner, P.; Heiland, J.: Space and Chaos-Expansion Galerkin POD Low-order Discretization of PDEs for Uncertainty Quantification. (2020)
Working Paper
Kour, K.; Dolgov, S.; Stoll, M.; Benner, P.: Efficient Structure-preserving Support Tensor Train Machine. (2020)
Working Paper
Sarna, N.; Giesselmann, J.; Benner, P.: Data-Driven Snapshot Calibration via Monotonic Feature Matching. (2020)
Working Paper
Weinhandl, R.; Benner, P.; Richter, T.: A Low-rank Method for Parameter-dependent Fluid-structure Interaction Discretizations with Hyperelasticity. (2020)
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