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ORCIDBenjamin Peherstorfer
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2020 – today
- 2024
[j34]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): 65 (2024)- [j33]Steffen W. R. Werner
, Benjamin Peherstorfer:
On the Sample Complexity of Stabilizing Linear Dynamical Systems from Data. Found. Comput. Math. 24(3): 955-987 (2024) - [j32]Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden:
Neural Galerkin schemes with active learning for high-dimensional evolution equations. J. Comput. Phys. 496: 112588 (2024) - [j31]Pawan Goyal, Benjamin Peherstorfer, Peter Benner:
Rank-Minimizing and Structured Model Inference. SIAM J. Sci. Comput. 46(3): 1879- (2024) - [j30]Paul Schwerdtner, Philipp Schulze, Jules Berman, Benjamin Peherstorfer:
Nonlinear Embeddings for Conserving Hamiltonians and Other Quantities with Neural Galerkin Schemes. SIAM J. Sci. Comput. 46(5): 583- (2024) - [c17]Jules Berman, Benjamin Peherstorfer:
CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations. ICML 2024 - [i38]Jules Berman, Benjamin Peherstorfer:
CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations. CoRR abs/2402.14646 (2024) - [i37]Paul Schwerdtner, Benjamin Peherstorfer:
Greedy construction of quadratic manifolds for nonlinear dimensionality reduction and nonlinear model reduction. CoRR abs/2403.06732 (2024) - [i36]Huan Zhang, Yifan Chen, Eric Vanden-Eijnden, Benjamin Peherstorfer:
Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations. CoRR abs/2404.01145 (2024) - [i35]Steffen W. R. Werner, Benjamin Peherstorfer:
System stabilization with policy optimization on unstable latent manifolds. CoRR abs/2407.06418 (2024) - [i34]Paul Schwerdtner, Prakash Mohan, Aleksandra Pachalieva, Julie Bessac, Daniel O'Malley, Benjamin Peherstorfer:
Online learning of quadratic manifolds from streaming data for nonlinear dimensionality reduction and nonlinear model reduction. CoRR abs/2409.02703 (2024) - 2023
- [j29]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: 224-239 (2023) - [j28]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: 112524 (2023) - [j27]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): 285-319 (2023) - [j26]Donsub Rim, Benjamin Peherstorfer, Kyle T. Mandli:
Manifold Approximations via Transported Subspaces: Model Reduction for Transport-Dominated Problems. SIAM J. Sci. Comput. 45(1): 170- (2023) - [j25]Steffen W. R. Werner, Michael L. Overton, Benjamin Peherstorfer:
Multifidelity Robust Controller Design with Gradient Sampling. SIAM J. Sci. Comput. 45(2): 933- (2023) - [j24]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) - [c16]Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer, Youssef M. Marzouk:
Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry. ICML 2023: 24214-24235 - [c15]Jules Berman, Benjamin Peherstorfer:
Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks. NeurIPS 2023 - [i33]Frederick Law, Antoine J. Cerfon, Benjamin Peherstorfer, Florian Wechsung:
Meta variance reduction for Monte Carlo estimation of energetic particle confinement during stellarator optimization. CoRR abs/2301.07280 (2023) - [i32]Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer, Youssef M. Marzouk:
Multi-fidelity covariance estimation in the log-Euclidean geometry. CoRR abs/2301.13749 (2023) - [i31]Pawan Goyal, Benjamin Peherstorfer, Peter Benner:
Rank-Minimizing and Structured Model Inference. CoRR abs/2302.09521 (2023) - [i30]Yuxiao Wen, Eric Vanden-Eijnden, Benjamin Peherstorfer:
Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes. CoRR abs/2306.15630 (2023) - [i29]Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer, Youssef M. Marzouk:
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices. CoRR abs/2307.12438 (2023) - [i28]Rodrigo Singh, Wayne Isaac Tan Uy, Benjamin Peherstorfer:
Lookahead data-gathering strategies for online adaptive model reduction of transport-dominated problems. CoRR abs/2307.14874 (2023) - [i27]Jules Berman, Benjamin Peherstorfer:
Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks. CoRR abs/2310.04867 (2023) - [i26]Paul Schwerdtner, Philipp Schulze, Jules Berman, Benjamin Peherstorfer:
Nonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemes. CoRR abs/2310.07485 (2023) - 2022
- [j23]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: 110898 (2022) - [c14]Nitin Shyamkumar, Serkan Gugercin, Benjamin Peherstorfer:
Towards context-aware learning for control: Balancing stability and model-learning error. ACC 2022: 4808-4813 - [i25]Steffen W. R. Werner, Benjamin Peherstorfer:
On the sample complexity of stabilizing linear dynamical systems from data. CoRR abs/2203.00474 (2022) - [i24]Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden:
Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations. CoRR abs/2203.01360 (2022) - [i23]Steffen W. R. Werner, Michael L. Overton, Benjamin Peherstorfer:
Multi-fidelity robust controller design with gradient sampling. CoRR abs/2205.15050 (2022) - [i22]Steffen W. R. Werner, Benjamin Peherstorfer:
Context-aware controller inference for stabilizing dynamical systems from scarce data. CoRR abs/2207.11049 (2022) - [i21]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 abs/2209.06957 (2022) - [i20]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 abs/2211.10835 (2022) - [i19]Wayne Isaac Tan Uy, Dirk Hartmann, Benjamin Peherstorfer:
Operator inference with roll outs for learning reduced models from scarce and low-quality data. CoRR abs/2212.01418 (2022) - [i18]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 abs/2212.03366 (2022) - 2021
- [j22]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): 91 (2021) - [c13]Terrence Alsup, Luca Venturi, Benjamin Peherstorfer:
Multilevel Stein variational gradient descent with applications to Bayesian inverse problems. MSML 2021: 93-117 - [c12]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 - [i17]Wayne Isaac Tan Uy, Benjamin Peherstorfer:
Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories. CoRR abs/2103.01362 (2021) - [i16]Terrence Alsup, Luca Venturi, Benjamin Peherstorfer:
Multilevel Stein variational gradient descent with applications to Bayesian inverse problems. CoRR abs/2104.01945 (2021) - [i15]Nihar Sawant, Boris Kramer, Benjamin Peherstorfer:
Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference. CoRR abs/2107.02597 (2021) - [i14]Wayne Isaac Tan Uy, Yuepeng Wang, Yuxiao Wen, Benjamin Peherstorfer:
Active operator inference for learning low-dimensional dynamical-system models from noisy data. CoRR abs/2107.09256 (2021) - [i13]Frederick Law, Antoine J. Cerfon, Benjamin Peherstorfer:
Accelerating the estimation of energetic particle confinement statistics in stellarators using multifidelity Monte Carlo. CoRR abs/2108.06408 (2021) - [i12]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 abs/2108.07799 (2021) - 2020
- [j21]Benjamin Peherstorfer:
Model Reduction for Transport-Dominated Problems via Online Adaptive Bases and Adaptive Sampling. SIAM J. Sci. Comput. 42(5): A2803-A2836 (2020) - [j20]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): A2837-A2864 (2020) - [j19]Benjamin Peherstorfer:
Sampling Low-Dimensional Markovian Dynamics for Preasymptotically Recovering Reduced Models from Data with Operator Inference. SIAM J. Sci. Comput. 42(5): A3489-A3515 (2020) - [c11]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: 2472-2477 - [i11]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 abs/2002.09726 (2020) - [i10]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 abs/2005.05890 (2020) - [i9]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 abs/2007.13977 (2020) - [i8]Terrence Alsup, Benjamin Peherstorfer:
Context-aware surrogate modeling for balancing approximation and sampling costs in multi-fidelity importance sampling and Bayesian inverse problems. CoRR abs/2010.11708 (2020)
2010 – 2019
- 2019
- [j18]Benjamin Peherstorfer, Youssef M. Marzouk:
A transport-based multifidelity preconditioner for Markov chain Monte Carlo. Adv. Comput. Math. 45(5): 2321-2348 (2019) - [j17]Boris Kramer, Alexandre Noll Marques, Benjamin Peherstorfer, Umberto Villa, Karen Willcox:
Multifidelity probability estimation via fusion of estimators. J. Comput. Phys. 392: 385-402 (2019) - [j16]Benjamin Peherstorfer:
Multifidelity Monte Carlo Estimation with Adaptive Low-Fidelity Models. SIAM/ASA J. Uncertain. Quantification 7(2): 579-603 (2019) - [i7]Boris Kramer, Alexandre Noll Marques, Benjamin Peherstorfer, Umberto Villa, Karen Willcox:
Multifidelity probability estimation via fusion of estimators. CoRR abs/1905.02679 (2019) - [i6]Benjamin Peherstorfer:
Sampling low-dimensional Markovian dynamics for pre-asymptotically recovering reduced models from data with operator inference. CoRR abs/1908.11233 (2019) - [i5]Zlatko Drmac, Benjamin Peherstorfer:
Learning low-dimensional dynamical-system models from noisy frequency-response data with Loewner rational interpolation. CoRR abs/1910.00110 (2019) - [i4]Elizabeth Qian, Boris Kramer, Benjamin Peherstorfer, Karen Willcox:
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems. CoRR abs/1912.08177 (2019) - [i3]Donsub Rim, Benjamin Peherstorfer, Kyle T. Mandli:
Manifold Approximations via Transported Subspaces: Model reduction for transport-dominated problems. CoRR abs/1912.13024 (2019) - 2018
- [j15]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): 683-706 (2018) - [j14]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): 737-761 (2018) - [j13]Benjamin Peherstorfer, Max D. Gunzburger, Karen Willcox:
Convergence analysis of multifidelity Monte Carlo estimation. Numerische Mathematik 139(3): 683-707 (2018) - [j12]Ralf Zimmermann, Benjamin Peherstorfer, Karen Willcox:
Geometric Subspace Updates with Applications to Online Adaptive Nonlinear Model Reduction. SIAM J. Matrix Anal. Appl. 39(1): 234-261 (2018) - [j11]Benjamin Peherstorfer, Karen Willcox, Max D. Gunzburger:
Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization. SIAM Rev. 60(3): 550-591 (2018) - [i2]Benjamin Peherstorfer, Karen Willcox, Max D. Gunzburger:
Survey of multifidelity methods in uncertainty propagation, inference, and optimization. CoRR abs/1806.10761 (2018) - [i1]Benjamin Peherstorfer:
Model reduction for transport-dominated problems via online adaptive bases and adaptive sampling. CoRR abs/1812.02094 (2018) - 2017
- [j10]Benjamin Peherstorfer, Boris Kramer, Karen Willcox:
Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models. J. Comput. Phys. 341: 61-75 (2017) - [j9]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): 1563-1586 (2017) - [j8]Benjamin Peherstorfer, Serkan Gugercin, Karen Willcox:
Data-Driven Reduced Model Construction with Time-Domain Loewner Models. SIAM J. Sci. Comput. 39(5) (2017) - 2016
- [j7]Benjamin Peherstorfer, Karen Willcox:
Dynamic data-driven model reduction: adapting reduced models from incomplete data. Adv. Model. Simul. Eng. Sci. 3(1): 11:1-11:22 (2016) - [j6]Benjamin Peherstorfer, Karen Willcox, Max D. Gunzburger:
Optimal Model Management for Multifidelity Monte Carlo Estimation. SIAM J. Sci. Comput. 38(5) (2016) - 2015
- [j5]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): 1365-1389 (2015) - [j4]Benjamin Peherstorfer, Karen Willcox:
Online Adaptive Model Reduction for Nonlinear Systems via Low-Rank Updates. SIAM J. Sci. Comput. 37(4) (2015) - [j3]Benjamin Peherstorfer, Stefan Zimmer, Christoph Zenger, Hans-Joachim Bungartz:
A Multigrid Method for Adaptive Sparse Grids. SIAM J. Sci. Comput. 37(5) (2015) - [c10]Benjamin Peherstorfer, Karen Willcox:
Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems. ICCS 2015: 2553-2562 - 2014
- [j2]Benjamin Peherstorfer, Daniel Butnaru, Karen Willcox, Hans-Joachim Bungartz:
Localized Discrete Empirical Interpolation Method. SIAM J. Sci. Comput. 36(1) (2014) - [c9]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: 2727-2732 - [c8]Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz:
Density Estimation with Adaptive Sparse Grids for Large Data Sets. SDM 2014: 443-451 - 2013
- [b1]Benjamin Peherstorfer:
Model order reduction of parametrized systems with sparse grid learning techniques. Technical University Munich, 2013, pp. 1-189 - [c7]Benjamin Peherstorfer, Julius Adorf, Dirk Pflüger, Hans-Joachim Bungartz:
Image Segmentation with Adaptive Sparse Grids. Australasian Conference on Artificial Intelligence 2013: 160-165 - [c6]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: 621-630 - 2012
- [c5]Daniel Butnaru, Benjamin Peherstorfer, Hans-Joachim Bungartz, Dirk Pflüger:
Fast Insight into High-Dimensional Parametrized Simulation Data. ICMLA (2) 2012: 265-270 - [c4]Alexander Heinecke, Benjamin Peherstorfer, Dirk Pflüger, Zhongwen Song:
Sparse grid classifiers as base learners for AdaBoost. HPCS 2012: 161-166 - [c3]Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz:
Clustering Based on Density Estimation with Sparse Grids. KI 2012: 131-142 - [c2]Benjamin Peherstorfer, Hans-Joachim Bungartz:
Semi-Coarsening in Space and Time for the Hierarchical Transformation Multigrid Method. ICCS 2012: 2000-2003 - 2011
- [c1]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: 112-121 - 2010
- [j1]Dirk Pflüger, Benjamin Peherstorfer, Hans-Joachim Bungartz:
Spatially adaptive sparse grids for high-dimensional data-driven problems. J. Complex. 26(5): 508-522 (2010)
Coauthor Index
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