Benjamin Peherstorfer

104 papers A* 4A 1C 2Misc 4Journal 85Unranked 7

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Nonlinear model reduction for transport-dominated problems. CoRR Jan S. Hesthaven, Benjamin Peherstorfer, Benjamin Unger
2025	J	`jnl`	An adaptive data sampling strategy for stabilizing dynamical systems via controller inference. CoRR Steffen W. R. Werner, Benjamin Peherstorfer
2025	J	`jnl`	DICE: Discrete inverse continuity equation for learning population dynamics. CoRR Tobias Blickhan, Jules Berman, Andrew Stuart, Benjamin Peherstorfer
2025	J	`jnl`	Empirical Sparse Regression on Quadratic Manifolds. SIAM J. Sci. Comput. Paul Schwerdtner, Serkan Gugercin, Benjamin Peherstorfer
2025	J	`jnl`	Filtered Neural Galerkin model reduction schemes for efficient propagation of initial condition uncertainties in digital twins. CoRR Zhiyang Ning, Benjamin Peherstorfer
2025	J	`jnl`	Hankel Singular Value Regularization for Highly Compressible State Space Models. CoRR Paul Schwerdtner, Jules Berman, Benjamin Peherstorfer
2025	J	`jnl`	Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices. SIAM J. Math. Data Sci. Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer, Youssef M. Marzouk
2025	J	`jnl`	Nonlinear model reduction with Neural Galerkin schemes on quadratic manifolds. J. Comput. Phys. Philipp Weder, Paul Schwerdtner, Benjamin Peherstorfer
2025	J	`jnl`	Operator Inference Aware Quadratic Manifolds with Isotropic Reduced Coordinates for Nonintrusive Model Reduction. CoRR Paul Schwerdtner, Prakash Mohan, Julie Bessac, Marc T. Henry de Frahan, Benjamin Peherstorfer
2025	J	`jnl`	Randomized time stepping of nonlinearly parametrized solutions of evolution problems. CoRR Yijun Dong, Paul Schwerdtner, Benjamin Peherstorfer
2024	A*	`conf`	CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations. ICML Jules Berman, Benjamin Peherstorfer
2024	J	`jnl`	CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations. CoRR Jules Berman, Benjamin Peherstorfer
2024	J	`jnl`	Empirical sparse regression on quadratic manifolds. CoRR Paul Schwerdtner, Serkan Gugercin, Benjamin Peherstorfer
2024	J	`jnl`	Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models. Adv. Comput. Math. Terrence Alsup, Tucker Hartland, Benjamin Peherstorfer, Noemi Petra
2024	J	`jnl`	Greedy construction of quadratic manifolds for nonlinear dimensionality reduction and nonlinear model reduction. CoRR Paul Schwerdtner, Benjamin Peherstorfer
2024	J	`jnl`	Neural Galerkin schemes with active learning for high-dimensional evolution equations. J. Comput. Phys. Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden
2024	J	`jnl`	Nonlinear Embeddings for Conserving Hamiltonians and Other Quantities with Neural Galerkin Schemes. SIAM J. Sci. Comput. Paul Schwerdtner, Philipp Schulze, Jules Berman, Benjamin Peherstorfer
2024	J	`jnl`	Nonlinear model reduction with Neural Galerkin schemes on quadratic manifolds. CoRR Philipp Weder, Paul Schwerdtner, Benjamin Peherstorfer
2024	J	`jnl`	On the Sample Complexity of Stabilizing Linear Dynamical Systems from Data. Found. Comput. Math. Steffen W. R. Werner, Benjamin Peherstorfer
2024	J	`jnl`	Online learning of quadratic manifolds from streaming data for nonlinear dimensionality reduction and nonlinear model reduction. CoRR Paul Schwerdtner, Prakash Mohan, Aleksandra Pachalieva, Julie Bessac, Daniel O'Malley, Benjamin Peherstorfer
2024	A*	`conf`	Parametric model reduction of mean-field and stochastic systems via higher-order action matching. NeurIPS Jules Berman, Tobias Blickhan, Benjamin Peherstorfer
2024	J	`jnl`	Parametric model reduction of mean-field and stochastic systems via higher-order action matching. CoRR Jules Berman, Tobias Blickhan, Benjamin Peherstorfer
2024	J	`jnl`	Rank-Minimizing and Structured Model Inference. SIAM J. Sci. Comput. Pawan Goyal, Benjamin Peherstorfer, Peter Benner
2024	J	`jnl`	Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations. CoRR Huan Zhang, Yifan Chen, Eric Vanden-Eijnden, Benjamin Peherstorfer
2024	J	`jnl`	System stabilization with policy optimization on unstable latent manifolds. CoRR Steffen W. R. Werner, Benjamin Peherstorfer
2023	J	`jnl`	Active Operator Inference for Learning Low-Dimensional Dynamical-System Models from Noisy Data. SIAM J. Sci. Comput. Wayne Isaac Tan Uy, Yuepeng Wang, Yuxiao Wen, Benjamin Peherstorfer
2023	J	`jnl`	Context-Aware Surrogate Modeling for Balancing Approximation and Sampling Costs in Multifidelity Importance Sampling and Bayesian Inverse Problems. SIAM/ASA J. Uncertain. Quantification Terrence Alsup, Benjamin Peherstorfer
2023	J	`jnl`	Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes. CoRR Yuxiao Wen, Eric Vanden-Eijnden, Benjamin Peherstorfer
2023	J	`jnl`	Lookahead data-gathering strategies for online adaptive model reduction of transport-dominated problems. CoRR Rodrigo Singh, Wayne Isaac Tan Uy, Benjamin Peherstorfer
2023	J	`jnl`	Manifold Approximations via Transported Subspaces: Model Reduction for Transport-Dominated Problems. SIAM J. Sci. Comput. Donsub Rim, Benjamin Peherstorfer, Kyle T. Mandli
2023	J	`jnl`	Meta variance reduction for Monte Carlo estimation of energetic particle confinement during stellarator optimization. CoRR Frederick Law, Antoine J. Cerfon, Benjamin Peherstorfer, Florian Wechsung
2023	J	`jnl`	Meta variance reduction for Monte Carlo estimation of energetic particle confinement during stellarator optimization. J. Comput. Phys. Frederick Law, Antoine J. Cerfon, Benjamin Peherstorfer, Florian Wechsung
2023	A*	`conf`	Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry. ICML Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer, Youssef M. Marzouk
2023	J	`jnl`	Multi-fidelity covariance estimation in the log-Euclidean geometry. CoRR Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer, Youssef M. Marzouk
2023	J	`jnl`	Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices. CoRR Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer, Youssef M. Marzouk
2023	J	`jnl`	Multifidelity Robust Controller Design with Gradient Sampling. SIAM J. Sci. Comput. Steffen W. R. Werner, Michael L. Overton, Benjamin Peherstorfer
2023	J	`jnl`	Nonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemes. CoRR Paul Schwerdtner, Philipp Schulze, Jules Berman, Benjamin Peherstorfer
2023	J	`jnl`	Operator inference with roll outs for learning reduced models from scarce and low-quality data. Comput. Math. Appl. Wayne Isaac Tan Uy, Dirk Hartmann, Benjamin Peherstorfer
2023	A*	`conf`	Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks. NeurIPS Jules Berman, Benjamin Peherstorfer
2023	J	`jnl`	Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks. CoRR Jules Berman, Benjamin Peherstorfer
2023	J	`jnl`	Rank-Minimizing and Structured Model Inference. CoRR Pawan Goyal, Benjamin Peherstorfer, Peter Benner
2022	J	`jnl`	Context-aware controller inference for stabilizing dynamical systems from scarce data. CoRR Steffen W. R. Werner, Benjamin Peherstorfer
2022	J	`jnl`	Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification. CoRR Ionut-Gabriel Farcas, Benjamin Peherstorfer, Tobias Neckel, Frank Jenko, Hans-Joachim Bungartz
2022	J	`jnl`	Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis. J. Comput. Phys. Julia Konrad, Ionut-Gabriel Farcas, Benjamin Peherstorfer, Alessandro Di Siena, Frank Jenko, Tobias Neckel, Hans-Joachim Bungartz
2022	J	`jnl`	Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models. CoRR Terrence Alsup, Tucker Hartland, Benjamin Peherstorfer, Noémi Petra
2022	J	`jnl`	Multi-fidelity robust controller design with gradient sampling. CoRR Steffen W. R. Werner, Michael L. Overton, Benjamin Peherstorfer
2022	J	`jnl`	Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations. CoRR Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden
2022	J	`jnl`	On the sample complexity of stabilizing linear dynamical systems from data. CoRR Steffen W. R. Werner, Benjamin Peherstorfer
2022	J	`jnl`	Operator inference with roll outs for learning reduced models from scarce and low-quality data. CoRR Wayne Isaac Tan Uy, Dirk Hartmann, Benjamin Peherstorfer
2022	J	`jnl`	Reduced models with nonlinear approximations of latent dynamics for model premixed flame problems. CoRR Wayne Isaac Tan Uy, Christopher R. Wentland, Cheng Huang, Benjamin Peherstorfer
2022	C	`conf`	Towards context-aware learning for control: Balancing stability and model-learning error. ACC Nitin Shyamkumar, Serkan Gugercin, Benjamin Peherstorfer
2021	J	`jnl`	Accelerating the estimation of energetic particle confinement statistics in stellarators using multifidelity Monte Carlo. CoRR Frederick Law, Antoine J. Cerfon, Benjamin Peherstorfer
2021	J	`jnl`	Active operator inference for learning low-dimensional dynamical-system models from noisy data. CoRR Wayne Isaac Tan Uy, Yuepeng Wang, Yuxiao Wen, Benjamin Peherstorfer
2021	—	`conf`	An Extensible Benchmark Suite for Learning to Simulate Physical Systems. NeurIPS Datasets and Benchmarks Karl Otness, Arvi Gjoka, Joan Bruna, Daniele Panozzo, Benjamin Peherstorfer, Teseo Schneider, Denis Zorin
2021	J	`jnl`	An Extensible Benchmark Suite for Learning to Simulate Physical Systems. CoRR Karl Otness, Arvi Gjoka, Joan Bruna, Daniele Panozzo, Benjamin Peherstorfer, Teseo Schneider, Denis Zorin
2021	—	`conf`	Multilevel Stein variational gradient descent with applications to Bayesian inverse problems. MSML Terrence Alsup, Luca Venturi, Benjamin Peherstorfer
2021	J	`jnl`	Multilevel Stein variational gradient descent with applications to Bayesian inverse problems. CoRR Terrence Alsup, Luca Venturi, Benjamin Peherstorfer
2021	J	`jnl`	Operator Inference of Non-Markovian Terms for Learning Reduced Models from Partially Observed State Trajectories. J. Sci. Comput. Wayne Isaac Tan Uy, Benjamin Peherstorfer
2021	J	`jnl`	Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories. CoRR Wayne Isaac Tan Uy, Benjamin Peherstorfer
2021	J	`jnl`	Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference. CoRR Nihar Sawant, Boris Kramer, Benjamin Peherstorfer
2020	J	`jnl`	Context-aware surrogate modeling for balancing approximation and sampling costs in multi-fidelity importance sampling and Bayesian inverse problems. CoRR Terrence Alsup, Benjamin Peherstorfer
2020	J	`jnl`	Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems. CoRR Donsub Rim, Luca Venturi, Joan Bruna, Benjamin Peherstorfer
2020	J	`jnl`	Model Reduction for Transport-Dominated Problems via Online Adaptive Bases and Adaptive Sampling. SIAM J. Sci. Comput. Benjamin Peherstorfer
2020	J	`jnl`	Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms. CoRR Peter Benner, Pawan Goyal, Boris Kramer, Benjamin Peherstorfer, Karen Willcox
2020	J	`jnl`	Probabilistic error estimation for non-intrusive reduced models learned from data of systems governed by linear parabolic partial differential equations. CoRR Wayne Isaac Tan Uy, Benjamin Peherstorfer
2020	C	`conf`	Quasi-Optimal Sampling to Learn Basis Updates for Online Adaptive Model Reduction with Adaptive Empirical Interpolation. ACC Alice Cortinovis, Daniel Kressner, Stefano Massei, Benjamin Peherstorfer
2020	J	`jnl`	Sampling Low-Dimensional Markovian Dynamics for Preasymptotically Recovering Reduced Models from Data with Operator Inference. SIAM J. Sci. Comput. Benjamin Peherstorfer
2020	J	`jnl`	Stability of Discrete Empirical Interpolation and Gappy Proper Orthogonal Decomposition with Randomized and Deterministic Sampling Points. SIAM J. Sci. Comput. Benjamin Peherstorfer, Zlatko Drmac, Serkan Gugercin
2019	J	`jnl`	A transport-based multifidelity preconditioner for Markov chain Monte Carlo. Adv. Comput. Math. Benjamin Peherstorfer, Youssef M. Marzouk
2019	J	`jnl`	Learning low-dimensional dynamical-system models from noisy frequency-response data with Loewner rational interpolation. CoRR Zlatko Drmac, Benjamin Peherstorfer
2019	J	`jnl`	Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems. CoRR Elizabeth Qian, Boris Kramer, Benjamin Peherstorfer, Karen Willcox
2019	J	`jnl`	Manifold Approximations via Transported Subspaces: Model reduction for transport-dominated problems. CoRR Donsub Rim, Benjamin Peherstorfer, Kyle T. Mandli
2019	J	`jnl`	Multifidelity Monte Carlo Estimation with Adaptive Low-Fidelity Models. SIAM/ASA J. Uncertain. Quantification Benjamin Peherstorfer
2019	J	`jnl`	Multifidelity probability estimation via fusion of estimators. CoRR Boris Kramer, Alexandre Noll Marques, Benjamin Peherstorfer, Umberto Villa, Karen Willcox
2019	J	`jnl`	Multifidelity probability estimation via fusion of estimators. J. Comput. Phys. Boris Kramer, Alexandre Noll Marques, Benjamin Peherstorfer, Umberto Villa, Karen Willcox
2019	J	`jnl`	Sampling low-dimensional Markovian dynamics for pre-asymptotically recovering reduced models from data with operator inference. CoRR Benjamin Peherstorfer
2018	J	`jnl`	Convergence analysis of multifidelity Monte Carlo estimation. Numerische Mathematik Benjamin Peherstorfer, Max D. Gunzburger, Karen Willcox
2018	J	`jnl`	Geometric Subspace Updates with Applications to Online Adaptive Nonlinear Model Reduction. SIAM J. Matrix Anal. Appl. Ralf Zimmermann, Benjamin Peherstorfer, Karen Willcox
2018	J	`jnl`	Model reduction for transport-dominated problems via online adaptive bases and adaptive sampling. CoRR Benjamin Peherstorfer
2018	J	`jnl`	Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices. SIAM/ASA J. Uncertain. Quantification Elizabeth Qian, Benjamin Peherstorfer, Daniel O'Malley, Velimir V. Vesselinov, Karen Willcox
2018	J	`jnl`	Multifidelity Preconditioning of the Cross-Entropy Method for Rare Event Simulation and Failure Probability Estimation. SIAM/ASA J. Uncertain. Quantification Benjamin Peherstorfer, Boris Kramer, Karen Willcox
2018	J	`jnl`	Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization. SIAM Rev. Benjamin Peherstorfer, Karen Willcox, Max D. Gunzburger
2018	J	`jnl`	Survey of multifidelity methods in uncertainty propagation, inference, and optimization. CoRR Benjamin Peherstorfer, Karen Willcox, Max D. Gunzburger
2017	J	`jnl`	Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models. J. Comput. Phys. Benjamin Peherstorfer, Boris Kramer, Karen Willcox
2017	J	`jnl`	Data-Driven Reduced Model Construction with Time-Domain Loewner Models. SIAM J. Sci. Comput. Benjamin Peherstorfer, Serkan Gugercin, Karen Willcox
2017	J	`jnl`	Feedback Control for Systems with Uncertain Parameters Using Online-Adaptive Reduced Models. SIAM J. Appl. Dyn. Syst. Boris Kramer, Benjamin Peherstorfer, Karen Willcox
2016	J	`jnl`	Dynamic data-driven model reduction: adapting reduced models from incomplete data. Adv. Model. Simul. Eng. Sci. Benjamin Peherstorfer, Karen Willcox
2016	J	`jnl`	Optimal Model Management for Multifidelity Monte Carlo Estimation. SIAM J. Sci. Comput. Benjamin Peherstorfer, Karen Willcox, Max D. Gunzburger
2015	J	`jnl`	A Multigrid Method for Adaptive Sparse Grids. SIAM J. Sci. Comput. Benjamin Peherstorfer, Stefan Zimmer, Christoph Zenger, Hans-Joachim Bungartz
2015	Misc	`conf`	Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems. ICCS Benjamin Peherstorfer, Karen Willcox
2015	J	`jnl`	Online Adaptive Model Reduction for Nonlinear Systems via Low-Rank Updates. SIAM J. Sci. Comput. Benjamin Peherstorfer, Karen Willcox
2015	J	`jnl`	Reduced models for sparse grid discretizations of the multi-asset Black-Scholes equation. Adv. Comput. Math. Benjamin Peherstorfer, Pablo Gómez, Hans-Joachim Bungartz
2014	A	`conf`	Density Estimation with Adaptive Sparse Grids for Large Data Sets. SDM Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz
2014	J	`jnl`	Localized Discrete Empirical Interpolation Method. SIAM J. Sci. Comput. Benjamin Peherstorfer, Daniel Butnaru, Karen Willcox, Hans-Joachim Bungartz
2014	—	`conf`	Parametric model order reduction by sparse-grid-based interpolation on matrix manifolds for multidimensional parameter spaces. ECC Matthias Geuß, Daniel Butnaru, Benjamin Peherstorfer, Hans-Joachim Bungartz, Boris Lohmann
2013	Misc	`conf`	Analysis of Car Crash Simulation Data with Nonlinear Machine Learning Methods. ICCS Bastian Bohn, Jochen Garcke, Rodrigo Iza-Teran, Alexander Paprotny, Benjamin Peherstorfer, Ulf Schepsmeier, Clemens-August Thole
2013	—	`conf`	Image Segmentation with Adaptive Sparse Grids. Australasian Conference on Artificial Intelligence Benjamin Peherstorfer, Julius Adorf, Dirk Pflüger, Hans-Joachim Bungartz
2013	—	`—`	Model order reduction of parametrized systems with sparse grid learning techniques. Benjamin Peherstorfer
2012	Misc	`conf`	Clustering Based on Density Estimation with Sparse Grids. KI Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz
2012	—	`conf`	Fast Insight into High-Dimensional Parametrized Simulation Data. ICMLA (2) Daniel Butnaru, Benjamin Peherstorfer, Hans-Joachim Bungartz, Dirk Pflüger
2012	Misc	`conf`	Semi-Coarsening in Space and Time for the Hierarchical Transformation Multigrid Method. ICCS Benjamin Peherstorfer, Hans-Joachim Bungartz
2012	—	`conf`	Sparse grid classifiers as base learners for AdaBoost. HPCS Alexander Heinecke, Benjamin Peherstorfer, Dirk Pflüger, Zhongwen Song
2011	—	`conf`	A Sparse-Grid-Based Out-of-Sample Extension for Dimensionality Reduction and Clustering with Laplacian Eigenmaps. Australasian Conference on Artificial Intelligence Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz
2010	J	`jnl`	Spatially adaptive sparse grids for high-dimensional data-driven problems. J. Complex. Dirk Pflüger, Benjamin Peherstorfer, Hans-Joachim Bungartz