Benjamin Peherstorfer
Orcid: 0000-0002-1558-6775
According to our database1,
Benjamin Peherstorfer
authored at least 70 papers
between 2010 and 2024.
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Bibliography
2024
Neural Galerkin schemes with active learning for high-dimensional evolution equations.
J. Comput. Phys., January, 2024
Greedy construction of quadratic manifolds for nonlinear dimensionality reduction and nonlinear model reduction.
CoRR, 2024
CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations.
CoRR, 2024
2023
Meta variance reduction for Monte Carlo estimation of energetic particle confinement during stellarator optimization.
J. Comput. Phys., December, 2023
Operator inference with roll outs for learning reduced models from scarce and low-quality data.
Comput. Math. Appl., September, 2023
Active Operator Inference for Learning Low-Dimensional Dynamical-System Models from Noisy Data.
SIAM J. Sci. Comput., August, 2023
SIAM J. Sci. Comput., April, 2023
Context-Aware Surrogate Modeling for Balancing Approximation and Sampling Costs in Multifidelity Importance Sampling and Bayesian Inverse Problems.
SIAM/ASA J. Uncertain. Quantification, March, 2023
Manifold Approximations via Transported Subspaces: Model Reduction for Transport-Dominated Problems.
SIAM J. Sci. Comput., February, 2023
Nonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemes.
CoRR, 2023
Lookahead data-gathering strategies for online adaptive model reduction of transport-dominated problems.
CoRR, 2023
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices.
CoRR, 2023
Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes.
CoRR, 2023
Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the International Conference on Machine Learning, 2023
2022
Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis.
J. Comput. Phys., 2022
Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models.
CoRR, 2022
Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification.
CoRR, 2022
Reduced models with nonlinear approximations of latent dynamics for model premixed flame problems.
CoRR, 2022
Context-aware controller inference for stabilizing dynamical systems from scarce data.
CoRR, 2022
Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations.
CoRR, 2022
CoRR, 2022
Towards context-aware learning for control: Balancing stability and model-learning error.
Proceedings of the American Control Conference, 2022
2021
Operator Inference of Non-Markovian Terms for Learning Reduced Models from Partially Observed State Trajectories.
J. Sci. Comput., 2021
Accelerating the estimation of energetic particle confinement statistics in stellarators using multifidelity Monte Carlo.
CoRR, 2021
Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference.
CoRR, 2021
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021
Multilevel Stein variational gradient descent with applications to Bayesian inverse problems.
Proceedings of the Mathematical and Scientific Machine Learning, 2021
2020
Stability of Discrete Empirical Interpolation and Gappy Proper Orthogonal Decomposition with Randomized and Deterministic Sampling Points.
SIAM J. Sci. Comput., 2020
Sampling Low-Dimensional Markovian Dynamics for Preasymptotically Recovering Reduced Models from Data with Operator Inference.
SIAM J. Sci. Comput., 2020
Model Reduction for Transport-Dominated Problems via Online Adaptive Bases and Adaptive Sampling.
SIAM J. Sci. Comput., 2020
Context-aware surrogate modeling for balancing approximation and sampling costs in multi-fidelity importance sampling and Bayesian inverse problems.
CoRR, 2020
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems.
CoRR, 2020
Probabilistic error estimation for non-intrusive reduced models learned from data of systems governed by linear parabolic partial differential equations.
CoRR, 2020
Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms.
CoRR, 2020
Quasi-Optimal Sampling to Learn Basis Updates for Online Adaptive Model Reduction with Adaptive Empirical Interpolation.
Proceedings of the 2020 American Control Conference, 2020
2019
SIAM/ASA J. Uncertain. Quantification, 2019
J. Comput. Phys., 2019
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems.
CoRR, 2019
Learning low-dimensional dynamical-system models from noisy frequency-response data with Loewner rational interpolation.
CoRR, 2019
Sampling low-dimensional Markovian dynamics for pre-asymptotically recovering reduced models from data with operator inference.
CoRR, 2019
Adv. Comput. Math., 2019
2018
Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization.
SIAM Rev., 2018
Geometric Subspace Updates with Applications to Online Adaptive Nonlinear Model Reduction.
SIAM J. Matrix Anal. Appl., 2018
Numerische Mathematik, 2018
SIAM/ASA J. Uncertain. Quantification, 2018
Multifidelity Preconditioning of the Cross-Entropy Method for Rare Event Simulation and Failure Probability Estimation.
SIAM/ASA J. Uncertain. Quantification, 2018
2017
SIAM J. Sci. Comput., 2017
Feedback Control for Systems with Uncertain Parameters Using Online-Adaptive Reduced Models.
SIAM J. Appl. Dyn. Syst., 2017
Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models.
J. Comput. Phys., 2017
2016
SIAM J. Sci. Comput., 2016
Adv. Model. Simul. Eng. Sci., 2016
2015
SIAM J. Sci. Comput., 2015
Reduced models for sparse grid discretizations of the multi-asset Black-Scholes equation.
Adv. Comput. Math., 2015
Detecting and Adapting to Parameter Changes for Reduced Models of Dynamic Data-driven Application Systems.
Proceedings of the International Conference on Computational Science, 2015
2014
Proceedings of the 2014 SIAM International Conference on Data Mining, 2014
Parametric model order reduction by sparse-grid-based interpolation on matrix manifolds for multidimensional parameter spaces.
Proceedings of the 13th European Control Conference, 2014
2013
PhD thesis, 2013
Proceedings of the International Conference on Computational Science, 2013
Proceedings of the AI 2013: Advances in Artificial Intelligence, 2013
2012
Semi-Coarsening in Space and Time for the Hierarchical Transformation Multigrid Method.
Proceedings of the International Conference on Computational Science, 2012
Proceedings of the KI 2012: Advances in Artificial Intelligence, 2012
Proceedings of the 2012 International Conference on High Performance Computing & Simulation, 2012
Proceedings of the 11th International Conference on Machine Learning and Applications, 2012
2011
A Sparse-Grid-Based Out-of-Sample Extension for Dimensionality Reduction and Clustering with Laplacian Eigenmaps.
Proceedings of the AI 2011: Advances in Artificial Intelligence, 2011
2010
J. Complex., 2010