Benjamin Peherstorfer

发表

A black-box method for parametric model order reduction

K. Willcox, M. Geuß, B. Lohmann, 2015 .

Stabilizing discrete empirical interpolation via randomized and deterministic oversampling

Benjamin Peherstorfer, Serkan Gugercin, Zlatko Drmavc, 2018, 1808.10473.

Convergence analysis of multifidelity Monte Carlo estimation

M. Gunzburger, K. Willcox, Benjamin Peherstorfer, 2018, Numerische Mathematik.

Data-driven operator inference for nonintrusive projection-based model reduction

K. Willcox, Benjamin Peherstorfer, B. Peherstorfer, 2016 .

A 2124 BENJAMIN PEHERSTORFER AND KAREN WILLCOX

K. Willcox, Benjamin Peherstorfer, 2015 .

Accelerating the estimation of collisionless energetic particle confinement statistics in stellarators using multifidelity Monte Carlo

Benjamin Peherstorfer, A. Cerfon, F. Law, 2021, Nuclear Fusion.

OPTIMAL MODEL MANAGEMENT FOR MULTIFIDELITY MONTE

K. Willcox, Benjamin Peherstorfer, Carlo Estimation, 2016 .

Multi-fidelity robust controller design with gradient sampling

Steffen W. R. Werner, M. Overton, Benjamin Peherstorfer, 2022, ArXiv.

Multifidelity probability estimation via fusion of estimators ACDL TR-2017-3

K. Willcox, Benjamin Peherstorfer, A. Marques, 2017 .

Context-aware controller inference for stabilizing dynamical systems from scarce data

Steffen W. R. Werner, Benjamin Peherstorfer, 2022, ArXiv.

A N E XTENSIBLE B ENCHMARK S UITE FOR L EARNING TO S IMULATE P HYSICAL S YSTEMS

Joan Bruna, D. Zorin, Benjamin Peherstorfer, 2021 .

Breaking the Kolmogorov Barrier with Nonlinear Model Reduction

Benjamin Peherstorfer, B. Peherstorfer, 2022, Notices of the American Mathematical Society.

Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models

N. Petra, Benjamin Peherstorfer, Tucker Hartland, 2022, 2212.03366.

Multi-fidelity robust controller design with gradient sampling

Steffen W. R. Werner, M. Overton, Benjamin Peherstorfer, 2022, SIAM J. Sci. Comput..

Meta variance reduction for Monte Carlo estimation of energetic particle confinement during stellarator optimization

Benjamin Peherstorfer, A. Cerfon, F. Wechsung, 2023, J. Comput. Phys..

Spatially adaptive sparse grids for high-dimensional data-driven problems

Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz, 2010, J. Complex..

Survey of multifidelity methods in uncertainty propagation, inference, and optimization

Benjamin Peherstorfer, Karen Willcox, Max D. Gunzburger, 2018, SIAM Rev..

New Loss Functions for Sparse Grid Classiers

Benjamin Peherstorfer, Florian Zacherl, Benjamin Peherstorfer, 2013 .

Analysis of Car Crash Simulation Data with Nonlinear Machine Learning Methods

Benjamin Peherstorfer, Clemens-August Thole, Alexander Paprotny, 2013, ICCS.

Multifidelity importance sampling

Tiangang Cui, Benjamin Peherstorfer, Youssef M. Marzouk, 2016 .

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems pdf

Benjamin Peherstorfer, Boris Kramer, Karen Willcox, 2019, Physica D: Nonlinear Phenomena.

Multifidelity Monte Carlo Estimation with Adaptive Low-Fidelity Models

Benjamin Peherstorfer, Benjamin Peherstorfer, B. Peherstorfer, 2019, SIAM/ASA J. Uncertain. Quantification.

Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices

Benjamin Peherstorfer, Velimir V. Vesselinov, Karen Willcox, 2018, SIAM/ASA J. Uncertain. Quantification.

Optimal Model Management for Multifidelity Monte Carlo Estimation

Benjamin Peherstorfer, Karen Willcox, Max D. Gunzburger, 2016, SIAM J. Sci. Comput..

Projection-based model reduction: Formulations for physics-based machine learning

Benjamin Peherstorfer, Karen Willcox, Laura Mainini, 2019, Computers & Fluids.

Localized Discrete Empirical Interpolation Method

Benjamin Peherstorfer, Hans-Joachim Bungartz, Karen Willcox, 2014, SIAM J. Sci. Comput..

Multifidelity probability estimation via fusion of estimators pdf

Benjamin Peherstorfer, Karen Willcox, Boris Kramer, 2019, J. Comput. Phys..

Multifidelity Preconditioning of the Cross-Entropy Method for Rare Event Simulation and Failure Probability Estimation

Benjamin Peherstorfer, Karen Willcox, Boris Kramer, 2018, SIAM/ASA J. Uncertain. Quantification.

Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models

Benjamin Peherstorfer, Karen Willcox, Boris Kramer, 2017, J. Comput. Phys..

Context-aware surrogate modeling for balancing approximation and sampling costs in multi-fidelity importance sampling and Bayesian inverse problems

Benjamin Peherstorfer, Terrence Alsup, B. Peherstorfer, 2020, SIAM/ASA J. Uncertain. Quantification.

Multifidelity Cross-Entropy Estimation of Conditional Value-at-Risk for Risk-Averse Design Optimization

Benjamin Peherstorfer, Karen Willcox, Anirban Chaudhuri, 2020 .

A transport-based multifidelity preconditioner for Markov chain Monte Carlo

Benjamin Peherstorfer, Youssef Marzouk, Y. Marzouk, 2018, Advances in Computational Mathematics.

Convergence analysis of multifidelity Monte Carlo estimation

Benjamin Peherstorfer, Karen Willcox, Max D. Gunzburger, 2018, Numerische Mathematik.

Multifidelity Monte Carlo estimation for large-scale uncertainty propagation

Benjamin Peherstorfer, Karen Willcox, Philip S. Beran, 2018 .

Manifold Approximations via Transported Subspaces: Model reduction for transport-dominated problems

Benjamin Peherstorfer, K. Mandli, Donsub Rim, 2019, SIAM J. Sci. Comput..

Model reduction for transport-dominated problems via online adaptive bases and adaptive sampling

Benjamin Peherstorfer, B. Peherstorfer, 2018, SIAM J. Sci. Comput..

Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms pdf

Benjamin Peherstorfer, Peter Benner, Pawan Goyal, 2020, ArXiv.

Sampling low-dimensional Markovian dynamics for pre-asymptotically recovering reduced models from data with operator inference pdf

Benjamin Peherstorfer, Benjamin Peherstorfer, B. Peherstorfer, 2019, SIAM J. Sci. Comput..

Learning low-dimensional dynamical-system models from noisy frequency-response data with Loewner rational interpolation pdf

Benjamin Peherstorfer, Zlatko Drmac, Z. Drmač, 2019, ArXiv.

Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification

H. Bungartz, F. Jenko, Benjamin Peherstorfer, 2022, Computer Methods in Applied Mechanics and Engineering.

Online Adaptive Model Reduction for Nonlinear Systems via Low-Rank Updates

Benjamin Peherstorfer, Karen Willcox, K. Willcox, 2015, SIAM J. Sci. Comput..

Reduced models with nonlinear approximations of latent dynamics for model premixed flame problems

Benjamin Peherstorfer, Cheng Huang, C. Wentland, 2022, ArXiv.

Quasi-Optimal Sampling to Learn Basis Updates for Online Adaptive Model Reduction with Adaptive Empirical Interpolation

Benjamin Peherstorfer, Daniel Kressner, Stefano Massei, 2020, 2020 American Control Conference (ACC).

Stability of Discrete Empirical Interpolation and Gappy Proper Orthogonal Decomposition with Randomized and Deterministic Sampling Points

Z. Drmač, Benjamin Peherstorfer, S. Gugercin, 2020, SIAM J. Sci. Comput..

Geometric Subspace Updates with Applications to Online Adaptive Nonlinear Model Reduction

Benjamin Peherstorfer, Karen Willcox, Ralf Zimmermann, 2018, SIAM J. Matrix Anal. Appl..

Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference pdf

Benjamin Peherstorfer, Boris Kramer, Nihar Sawant, 2021, Computer Methods in Applied Mechanics and Engineering.

Active operator inference for learning low-dimensional dynamical-system models from noisy data pdf

Benjamin Peherstorfer, Yuepeng Wang, Wayne Isaac Tan Uy, 2021, SIAM J. Sci. Comput..

An Extensible Benchmark Suite for Learning to Simulate Physical Systems pdf

Joan Bruna, Benjamin Peherstorfer, Daniele Panozzo, 2021, NeurIPS Datasets and Benchmarks.

A Sparse-Grid-Based Out-of-Sample Extension for Dimensionality Reduction and Clustering with Laplacian Eigenmaps

Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz, 2011, Australasian Conference on Artificial Intelligence.

Selected Recent Applications of Sparse Grids

Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz, 2015 .

Dynamic data-driven model reduction: adapting reduced models from incomplete data

Benjamin Peherstorfer, Karen Willcox, K. Willcox, 2016, Adv. Model. Simul. Eng. Sci..

Unbiased multi-fidelity estimate of failure probability of a free plane jet

Benjamin Peherstorfer, Karen Willcox, Boris Kramer, 2017 .

Context-aware controller inference for stabilizing dynamical systems from scarce data

Steffen W. R. Werner, Benjamin Peherstorfer, B. Peherstorfer, 2022, Proceedings of the Royal Society A.

Feedback Control for Systems with Uncertain Parameters Using Online-Adaptive Reduced Models

Benjamin Peherstorfer, Karen Willcox, Boris Kramer, 2017, SIAM J. Appl. Dyn. Syst..

Fast Insight into High-Dimensional Parametrized Simulation Data

Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz, 2012, 2012 11th International Conference on Machine Learning and Applications.

Accelerating the estimation of collisionless energetic particle confinement statistics in stellarators using multifidelity Monte Carlo pdf

Benjamin Peherstorfer, Antoine Cerfon, Frederick Law, 2021, Nuclear Fusion.

Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems pdf

Benjamin Peherstorfer, Joan Bruna, Donsub Rim, 2020, ArXiv.

Dynamic data-driven reduced-order models

Benjamin Peherstorfer, Karen Willcox, K. Willcox, 2015 .

On the Sample Complexity of Stabilizing Linear Dynamical Systems from Data

Steffen W. R. Werner, Benjamin Peherstorfer, B. Peherstorfer, 2022, Found. Comput. Math..

Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems

Benjamin Peherstorfer, Karen Willcox, K. Willcox, 2015, ICCS.

Data-Driven Reduced Model Construction with Time-Domain Loewner Models

Benjamin Peherstorfer, Karen Willcox, Serkan Gugercin, 2017, SIAM J. Sci. Comput..

Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations

Joan Bruna, E. Vanden-Eijnden, Benjamin Peherstorfer, 2022, ArXiv.

Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes

E. Vanden-Eijnden, Benjamin Peherstorfer, Yu-Hui Wen, 2023, ArXiv.

Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices

Y. Marzouk, Benjamin Peherstorfer, Terrence Alsup, 2023, ArXiv.

Multi-fidelity covariance estimation in the log-Euclidean geometry

Y. Marzouk, Benjamin Peherstorfer, Terrence Alsup, 2023, ICML.

Rank-Minimizing and Structured Model Inference

Benjamin Peherstorfer, P. Benner, P. Goyal, 2023, SIAM J. Sci. Comput..

Operator inference with roll outs for learning reduced models from scarce and low-quality data

Benjamin Peherstorfer, D. Hartmann, W. I. Uy, 2022, Comput. Math. Appl..

Operator Inference of Non-Markovian Terms for Learning Reduced Models from Partially Observed State Trajectories

Benjamin Peherstorfer, W. I. Uy, 2021, Journal of Scientific Computing.

Operator Inference of Non-Markovian Terms for Learning Reduced Models from Partially Observed State Trajectories

Benjamin Peherstorfer, W. I. Uy, 2021, J. Sci. Comput..

Probabilistic error estimation for non-intrusive reduced models learned from data of systems governed by linear parabolic partial differential equations pdf

Benjamin Peherstorfer, Wayne Isaac Tan Uy, Benjamin Peherstorfer, 2020, ESAIM: Mathematical Modelling and Numerical Analysis.

Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis

H. Bungartz, F. Jenko, Benjamin Peherstorfer, 2021, J. Comput. Phys..

Lookahead data-gathering strategies for online adaptive model reduction of transport-dominated problems

Benjamin Peherstorfer, W. I. Uy, Rodrigo Singh, 2023, ArXiv.