Benjamin Peherstorfer

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 71

Top Venues

CoRR

SIAM J. Sci. Comput.

J. Comput. Phys.

SIAM/ASA J. Uncertain. Quantification

Technical University Munich (PHD)

Education History

Technical University Munich

PHD

Publications

Steffen W. R. Werner, Benjamin Peherstorfer
On the Sample Complexity of Stabilizing Linear Dynamical Systems from Data. Found. Comput. Math. 24 (3) (2024)
Terrence Alsup, Tucker Hartland, Benjamin Peherstorfer, Noemi Petra
Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models. Adv. Comput. Math. 50 (4) (2024)
Huan Zhang, Yifan Chen, Eric Vanden-Eijnden, Benjamin Peherstorfer
Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations. CoRR (2024)
Paul Schwerdtner, Benjamin Peherstorfer
Greedy construction of quadratic manifolds for nonlinear dimensionality reduction and nonlinear model reduction. CoRR (2024)
Pawan Goyal, Benjamin Peherstorfer, Peter Benner
Rank-Minimizing and Structured Model Inference. SIAM J. Sci. Comput. 46 (3) (2024)
Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden
Neural Galerkin schemes with active learning for high-dimensional evolution equations. J. Comput. Phys. 496 (2024)
Steffen W. R. Werner, Benjamin Peherstorfer
System stabilization with policy optimization on unstable latent manifolds. CoRR (2024)
Jules Berman, Benjamin Peherstorfer
CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations. CoRR (2024)
Wayne Isaac Tan Uy, Dirk Hartmann, Benjamin Peherstorfer
Operator inference with roll outs for learning reduced models from scarce and low-quality data. Comput. Math. Appl. 145 (2023)
Pawan Goyal, Benjamin Peherstorfer, Peter Benner
Rank-Minimizing and Structured Model Inference. CoRR (2023)
Wayne Isaac Tan Uy, Yuepeng Wang, Yuxiao Wen, Benjamin Peherstorfer
Active Operator Inference for Learning Low-Dimensional Dynamical-System Models from Noisy Data. SIAM J. Sci. Comput. 45 (4) (2023)
Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer, Youssef M. Marzouk
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices. CoRR (2023)
Frederick Law, Antoine J. Cerfon, Benjamin Peherstorfer, Florian Wechsung
Meta variance reduction for Monte Carlo estimation of energetic particle confinement during stellarator optimization. J. Comput. Phys. 495 (2023)
Terrence Alsup, Benjamin Peherstorfer
Context-Aware Surrogate Modeling for Balancing Approximation and Sampling Costs in Multifidelity Importance Sampling and Bayesian Inverse Problems. SIAM/ASA J. Uncertain. Quantification 11 (1) (2023)
Jules Berman, Benjamin Peherstorfer
Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks. NeurIPS (2023)
Donsub Rim, Benjamin Peherstorfer, Kyle T. Mandli
Manifold Approximations via Transported Subspaces: Model Reduction for Transport-Dominated Problems. SIAM J. Sci. Comput. 45 (1) (2023)
Paul Schwerdtner, Philipp Schulze, Jules Berman, Benjamin Peherstorfer
Nonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemes. CoRR (2023)
Frederick Law, Antoine J. Cerfon, Benjamin Peherstorfer, Florian Wechsung
Meta variance reduction for Monte Carlo estimation of energetic particle confinement during stellarator optimization. CoRR (2023)
Rodrigo Singh, Wayne Isaac Tan Uy, Benjamin Peherstorfer
Lookahead data-gathering strategies for online adaptive model reduction of transport-dominated problems. CoRR (2023)
Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer, Youssef M. Marzouk
Multi-fidelity covariance estimation in the log-Euclidean geometry. CoRR (2023)
Steffen W. R. Werner, Michael L. Overton, Benjamin Peherstorfer
Multifidelity Robust Controller Design with Gradient Sampling. SIAM J. Sci. Comput. 45 (2) (2023)
Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer, Youssef M. Marzouk
Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry. ICML (2023)
Yuxiao Wen, Eric Vanden-Eijnden, Benjamin Peherstorfer
Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes. CoRR (2023)
Jules Berman, Benjamin Peherstorfer
Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks. CoRR (2023)
Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden
Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations. CoRR (2022)
Ionut-Gabriel Farcas, Benjamin Peherstorfer, Tobias Neckel, Frank Jenko, Hans-Joachim Bungartz
Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification. CoRR (2022)
Steffen W. R. Werner, Benjamin Peherstorfer
Context-aware controller inference for stabilizing dynamical systems from scarce data. CoRR (2022)
Wayne Isaac Tan Uy, Dirk Hartmann, Benjamin Peherstorfer
Operator inference with roll outs for learning reduced models from scarce and low-quality data. CoRR (2022)
Julia Konrad, Ionut-Gabriel Farcas, Benjamin Peherstorfer, Alessandro Di Siena, Frank Jenko, Tobias Neckel, Hans-Joachim Bungartz
Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis. J. Comput. Phys. 451 (2022)
Terrence Alsup, Tucker Hartland, Benjamin Peherstorfer, Noémi Petra
Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models. CoRR (2022)
Wayne Isaac Tan Uy, Christopher R. Wentland, Cheng Huang, Benjamin Peherstorfer
Reduced models with nonlinear approximations of latent dynamics for model premixed flame problems. CoRR (2022)
Steffen W. R. Werner, Michael L. Overton, Benjamin Peherstorfer
Multi-fidelity robust controller design with gradient sampling. CoRR (2022)
Nitin Shyamkumar, Serkan Gugercin, Benjamin Peherstorfer
Towards context-aware learning for control: Balancing stability and model-learning error. ACC (2022)
Steffen W. R. Werner, Benjamin Peherstorfer
On the sample complexity of stabilizing linear dynamical systems from data. CoRR (2022)
Nihar Sawant, Boris Kramer, Benjamin Peherstorfer
Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference. CoRR (2021)
Terrence Alsup, Luca Venturi, Benjamin Peherstorfer
Multilevel Stein variational gradient descent with applications to Bayesian inverse problems. CoRR (2021)
Karl Otness, Arvi Gjoka, Joan Bruna, Daniele Panozzo, Benjamin Peherstorfer, Teseo Schneider, Denis Zorin
An Extensible Benchmark Suite for Learning to Simulate Physical Systems. CoRR (2021)
Karl Otness, Arvi Gjoka, Joan Bruna, Daniele Panozzo, Benjamin Peherstorfer, Teseo Schneider, Denis Zorin
An Extensible Benchmark Suite for Learning to Simulate Physical Systems. NeurIPS Datasets and Benchmarks (2021)
Wayne Isaac Tan Uy, Benjamin Peherstorfer
Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories. CoRR (2021)
Frederick Law, Antoine J. Cerfon, Benjamin Peherstorfer
Accelerating the estimation of energetic particle confinement statistics in stellarators using multifidelity Monte Carlo. CoRR (2021)
Wayne Isaac Tan Uy, Benjamin Peherstorfer
Operator Inference of Non-Markovian Terms for Learning Reduced Models from Partially Observed State Trajectories. J. Sci. Comput. 88 (3) (2021)
Terrence Alsup, Luca Venturi, Benjamin Peherstorfer
Multilevel Stein variational gradient descent with applications to Bayesian inverse problems. MSML (2021)
Wayne Isaac Tan Uy, Yuepeng Wang, Yuxiao Wen, Benjamin Peherstorfer
Active operator inference for learning low-dimensional dynamical-system models from noisy data. CoRR (2021)
Alice Cortinovis, Daniel Kressner, Stefano Massei, Benjamin Peherstorfer
Quasi-Optimal Sampling to Learn Basis Updates for Online Adaptive Model Reduction with Adaptive Empirical Interpolation. ACC (2020)
Benjamin Peherstorfer, Zlatko Drmac, Serkan Gugercin
Stability of Discrete Empirical Interpolation and Gappy Proper Orthogonal Decomposition with Randomized and Deterministic Sampling Points. SIAM J. Sci. Comput. 42 (5) (2020)
Benjamin Peherstorfer
Sampling Low-Dimensional Markovian Dynamics for Preasymptotically Recovering Reduced Models from Data with Operator Inference. SIAM J. Sci. Comput. 42 (5) (2020)
Donsub Rim, Luca Venturi, Joan Bruna, Benjamin Peherstorfer
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems. CoRR (2020)
Wayne Isaac Tan Uy, Benjamin Peherstorfer
Probabilistic error estimation for non-intrusive reduced models learned from data of systems governed by linear parabolic partial differential equations. CoRR (2020)
Benjamin Peherstorfer
Model Reduction for Transport-Dominated Problems via Online Adaptive Bases and Adaptive Sampling. SIAM J. Sci. Comput. 42 (5) (2020)
Terrence Alsup, Benjamin Peherstorfer
Context-aware surrogate modeling for balancing approximation and sampling costs in multi-fidelity importance sampling and Bayesian inverse problems. CoRR (2020)
Peter Benner, Pawan Goyal, Boris Kramer, Benjamin Peherstorfer, Karen Willcox
Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms. CoRR (2020)
Donsub Rim, Benjamin Peherstorfer, Kyle T. Mandli
Manifold Approximations via Transported Subspaces: Model reduction for transport-dominated problems. CoRR (2019)
Benjamin Peherstorfer
Multifidelity Monte Carlo Estimation with Adaptive Low-Fidelity Models. SIAM/ASA J. Uncertain. Quantification 7 (2) (2019)
Zlatko Drmac, Benjamin Peherstorfer
Learning low-dimensional dynamical-system models from noisy frequency-response data with Loewner rational interpolation. CoRR (2019)
Elizabeth Qian, Boris Kramer, Benjamin Peherstorfer, Karen Willcox
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems. CoRR (2019)
Boris Kramer, Alexandre Noll Marques, Benjamin Peherstorfer, Umberto Villa, Karen Willcox
Multifidelity probability estimation via fusion of estimators. J. Comput. Phys. 392 (2019)
Benjamin Peherstorfer
Sampling low-dimensional Markovian dynamics for pre-asymptotically recovering reduced models from data with operator inference. CoRR (2019)
Benjamin Peherstorfer, Youssef M. Marzouk
A transport-based multifidelity preconditioner for Markov chain Monte Carlo. Adv. Comput. Math. 45 (5) (2019)
Boris Kramer, Alexandre Noll Marques, Benjamin Peherstorfer, Umberto Villa, Karen Willcox
Multifidelity probability estimation via fusion of estimators. CoRR (2019)
Benjamin Peherstorfer, Karen Willcox, Max D. Gunzburger
Survey of multifidelity methods in uncertainty propagation, inference, and optimization. CoRR (2018)
Benjamin Peherstorfer, Boris Kramer, Karen Willcox
Multifidelity Preconditioning of the Cross-Entropy Method for Rare Event Simulation and Failure Probability Estimation. SIAM/ASA J. Uncertain. Quantification 6 (2) (2018)
Elizabeth Qian, Benjamin Peherstorfer, Daniel O'Malley, Velimir V. Vesselinov, Karen Willcox
Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices. SIAM/ASA J. Uncertain. Quantification 6 (2) (2018)
Benjamin Peherstorfer
Model reduction for transport-dominated problems via online adaptive bases and adaptive sampling. CoRR (2018)
Benjamin Peherstorfer, Karen Willcox, Max D. Gunzburger
Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization. SIAM Rev. 60 (3) (2018)
Ralf Zimmermann, Benjamin Peherstorfer, Karen Willcox
Geometric Subspace Updates with Applications to Online Adaptive Nonlinear Model Reduction. SIAM J. Matrix Anal. Appl. 39 (1) (2018)
Benjamin Peherstorfer, Max D. Gunzburger, Karen Willcox
Convergence analysis of multifidelity Monte Carlo estimation. Numerische Mathematik 139 (3) (2018)
Benjamin Peherstorfer, Serkan Gugercin, Karen Willcox
Data-Driven Reduced Model Construction with Time-Domain Loewner Models. SIAM J. Sci. Comput. 39 (5) (2017)
Boris Kramer, Benjamin Peherstorfer, Karen Willcox
Feedback Control for Systems with Uncertain Parameters Using Online-Adaptive Reduced Models. SIAM J. Appl. Dyn. Syst. 16 (3) (2017)
Benjamin Peherstorfer, Boris Kramer, Karen Willcox
Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models. J. Comput. Phys. 341 (2017)
Benjamin Peherstorfer, Karen Willcox
Dynamic data-driven model reduction: adapting reduced models from incomplete data. Adv. Model. Simul. Eng. Sci. 3 (1) (2016)
Benjamin Peherstorfer, Karen Willcox, Max D. Gunzburger
Optimal Model Management for Multifidelity Monte Carlo Estimation. SIAM J. Sci. Comput. 38 (5) (2016)
Benjamin Peherstorfer, Karen Willcox
Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems. ICCS (2015)
Benjamin Peherstorfer, Stefan Zimmer, Christoph Zenger, Hans-Joachim Bungartz
A Multigrid Method for Adaptive Sparse Grids. SIAM J. Sci. Comput. 37 (5) (2015)
Benjamin Peherstorfer, Karen Willcox
Online Adaptive Model Reduction for Nonlinear Systems via Low-Rank Updates. SIAM J. Sci. Comput. 37 (4) (2015)
Benjamin Peherstorfer, Pablo Gómez, Hans-Joachim Bungartz
Reduced models for sparse grid discretizations of the multi-asset Black-Scholes equation. Adv. Comput. Math. 41 (5) (2015)
Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz
Density Estimation with Adaptive Sparse Grids for Large Data Sets. SDM (2014)
Matthias Geuß, Daniel Butnaru, Benjamin Peherstorfer, Hans-Joachim Bungartz, Boris Lohmann
Parametric model order reduction by sparse-grid-based interpolation on matrix manifolds for multidimensional parameter spaces. ECC (2014)
Benjamin Peherstorfer, Daniel Butnaru, Karen Willcox, Hans-Joachim Bungartz
Localized Discrete Empirical Interpolation Method. SIAM J. Sci. Comput. 36 (1) (2014)
Bastian Bohn, Jochen Garcke, Rodrigo Iza-Teran, Alexander Paprotny, Benjamin Peherstorfer, Ulf Schepsmeier, Clemens-August Thole
Analysis of Car Crash Simulation Data with Nonlinear Machine Learning Methods. ICCS (2013)
Benjamin Peherstorfer, Julius Adorf, Dirk Pflüger, Hans-Joachim Bungartz
Image Segmentation with Adaptive Sparse Grids. Australasian Conference on Artificial Intelligence (2013)
Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz
Clustering Based on Density Estimation with Sparse Grids. KI (2012)
Benjamin Peherstorfer, Hans-Joachim Bungartz
Semi-Coarsening in Space and Time for the Hierarchical Transformation Multigrid Method. ICCS (2012)
Alexander Heinecke, Benjamin Peherstorfer, Dirk Pflüger, Zhongwen Song
Sparse grid classifiers as base learners for AdaBoost. HPCS (2012)
Daniel Butnaru, Benjamin Peherstorfer, Hans-Joachim Bungartz, Dirk Pflüger
Fast Insight into High-Dimensional Parametrized Simulation Data. ICMLA (2) (2012)
Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz
A Sparse-Grid-Based Out-of-Sample Extension for Dimensionality Reduction and Clustering with Laplacian Eigenmaps. Australasian Conference on Artificial Intelligence (2011)
Dirk Pflüger, Benjamin Peherstorfer, Hans-Joachim Bungartz
Spatially adaptive sparse grids for high-dimensional data-driven problems. J. Complex. 26 (5) (2010)

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Publication Activity (10 Years)

Top Topics

Top Venues

Benjamin Peherstorfer