default search action
Benjamin Peherstorfer
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
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
Rank-Minimizing and Structured Model Inference. SIAM J. Sci. Comput. 46(3): 1879- (2024) - [j30]Paul Schwerdtner
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 - [i39]Jules Berman, Benjamin Peherstorfer:
CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations. CoRR abs/2402.14646 (2024) - [i38]Paul Schwerdtner, Benjamin Peherstorfer:
Greedy construction of quadratic manifolds for nonlinear dimensionality reduction and nonlinear model reduction. CoRR abs/2403.06732 (2024) - [i37]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) - [i36]Steffen W. R. Werner
System stabilization with policy optimization on unstable latent manifolds. CoRR abs/2407.06418 (2024) - [i35]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) - [i34]Jules Berman, Tobias Blickhan, Benjamin Peherstorfer:
Parametric model reduction of mean-field and stochastic systems via higher-order action matching. CoRR abs/2410.12000 (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
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
Manifold Approximations via Transported Subspaces: Model Reduction for Transport-Dominated Problems. SIAM J. Sci. Comput. 45(1): 170- (2023) - [j25]Steffen W. R. Werner
Multifidelity Robust Controller Design with Gradient Sampling. SIAM J. Sci. Comput. 45(2): 933- (2023) - [j24]Wayne Isaac Tan Uy
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
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
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
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
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
Multi-fidelity robust controller design with gradient sampling. CoRR abs/2205.15050 (2022) - [i22]Steffen W. R. Werner
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
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
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
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
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
Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices. SIAM/ASA J. Uncertain. Quantification 6(2): 683-706 (2018) - [j14]Benjamin Peherstorfer, Boris Kramer
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
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
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
Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models. J. Comput. Phys. 341: 61-75 (2017) - [j9]Boris Kramer
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
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
Optimal Model Management for Multifidelity Monte Carlo Estimation. SIAM J. Sci. Comput. 38(5) (2016) - 2015
- [j5]Benjamin Peherstorfer, Pablo Gómez
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
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
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
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-12-01 01:07 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: