270 citations · 618 across the 36 of their papers we have counts for
36 papers
An adaptive data sampling strategy for stabilizing dynamical systems via controller inference
Steffen W. R. Werner, Benjamin Peherstorfer
Learning stabilizing controllers from data is an important task in engineering applications; however, collecting informative data is challenging because unstable systems often lead…
System stabilization with policy optimization on unstable latent manifolds
Steffen W. R. Werner, Benjamin Peherstorfer
Stability is a basic requirement when studying the behavior of dynamical systems. However, stabilizing dynamical systems via reinforcement learning is challenging because only litt…
Lookahead data-gathering strategies for online adaptive model reduction of transport-dominated problems
Rodrigo Singh, Wayne Isaac Tan Uy, Benjamin Peherstorfer
Online adaptive model reduction efficiently reduces numerical models of transport-dominated problems by updating reduced spaces over time, which leads to nonlinear approximations o…
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices
Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer +1
We introduce a multifidelity estimator of covariance matrices formulated as the solution to a regression problem on the manifold of symmetric positive definite matrices. The estima…
Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes
Yuxiao Wen, Eric Vanden-Eijnden, Benjamin Peherstorfer
Training nonlinear parametrizations such as deep neural networks to numerically approximate solutions of partial differential equations is often based on minimizing a loss that inc…
Rank-Minimizing and Structured Model Inference
Pawan Goyal, Benjamin Peherstorfer, Peter Benner
While extracting information from data with machine learning plays an increasingly important role, physical laws and other first principles continue to provide critical insights ab…
Meta variance reduction for Monte Carlo estimation of energetic particle confinement during stellarator optimization
Frederick Law, Antoine Cerfon, Benjamin Peherstorfer +1
This work introduces meta estimators that combine multiple multifidelity techniques based on control variates, importance sampling, and information reuse to yield a quasi-multiplic…
Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry
Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer +1
We introduce a multi-fidelity estimator of covariance matrices that employs the log-Euclidean geometry of the symmetric positive-definite manifold. The estimator fuses samples from…
Operator inference with roll outs for learning reduced models from scarce and low-quality data
Wayne Isaac Tan Uy, Dirk Hartmann, Benjamin Peherstorfer
Data-driven modeling has become a key building block in computational science and engineering. However, data that are available in science and engineering are typically scarce, oft…
Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models
Terrence Alsup, Tucker Hartland, Benjamin Peherstorfer +1
Multilevel Stein variational gradient descent is a method for particle-based variational inference that leverages hierarchies of surrogate target distributions with varying costs a…
Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification
Ionut-Gabriel Farcas, Benjamin Peherstorfer, Tobias Neckel +2
Multi-fidelity Monte Carlo methods leverage low-fidelity and surrogate models for variance reduction to make tractable uncertainty quantification even when numerically simulating t…
Reduced models with nonlinear approximations of latent dynamics for model premixed flame problems
Wayne Isaac Tan Uy, Christopher R. Wentland, Cheng Huang +1
Efficiently reducing models of chemically reacting flows is often challenging because their characteristic features such as sharp gradients in the flow fields and couplings over va…
Context-aware controller inference for stabilizing dynamical systems from scarce data
Steffen W. R. Werner, Benjamin Peherstorfer
This work introduces a data-driven control approach for stabilizing high-dimensional dynamical systems from scarce data. The proposed context-aware controller inference approach is…
Multi-fidelity robust controller design with gradient sampling
Steffen W. R. Werner, Michael L. Overton, Benjamin Peherstorfer
Robust controllers that stabilize dynamical systems even under disturbances and noise are often formulated as solutions of nonsmooth, nonconvex optimization problems. While methods…
Neural Galerkin Schemes with Active Learning for High-Dimensional Evolution Equations
Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden
Deep neural networks have been shown to provide accurate function approximations in high dimensions. However, fitting network parameters requires informative training data that are…
On the sample complexity of stabilizing linear dynamical systems from data
Steffen W. R. Werner, Benjamin Peherstorfer
Learning controllers from data for stabilizing dynamical systems typically follows a two step process of first identifying a model and then constructing a controller based on the i…
Accelerating the estimation of energetic particle confinement statistics in stellarators using multifidelity Monte Carlo
Frederick Law, Antoine Cerfon, Benjamin Peherstorfer
In the design of stellarators, energetic particle confinement is a critical point of concern which remains challenging to study from a numerical point of view. Standard Monte Carlo…
An Extensible Benchmark Suite for Learning to Simulate Physical Systems
Karl Otness, Arvi Gjoka, Joan Bruna +4
Simulating physical systems is a core component of scientific computing, encompassing a wide range of physical domains and applications. Recently, there has been a surge in data-dr…
Active operator inference for learning low-dimensional dynamical-system models from noisy data
Wayne Isaac Tan Uy, Yuepeng Wang, Yuxiao Wen +1
Noise poses a challenge for learning dynamical-system models because already small variations can distort the dynamics described by trajectory data. This work builds on operator in…
Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference
Nihar Sawant, Boris Kramer, Benjamin Peherstorfer
Operator inference learns low-dimensional dynamical-system models with polynomial nonlinear terms from trajectories of high-dimensional physical systems (non-intrusive model reduct…
Multilevel Stein variational gradient descent with applications to Bayesian inverse problems
Terrence Alsup, Luca Venturi, Benjamin Peherstorfer
This work presents a multilevel variant of Stein variational gradient descent to more efficiently sample from target distributions. The key ingredient is a sequence of distribution…
Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories
Wayne Isaac Tan Uy, Benjamin Peherstorfer
This work introduces a non-intrusive model reduction approach for learning reduced models from partially observed state trajectories of high-dimensional dynamical systems. The prop…
Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis
Julia Konrad, Ionut-Gabriel Farcas, Benjamin Peherstorfer +4
The linear micro-instabilities driving turbulent transport in magnetized fusion plasmas (as well as the respective nonlinear saturation mechanisms) are known to be sensitive with r…
Context-aware surrogate modeling for balancing approximation and sampling costs in multi-fidelity importance sampling and Bayesian inverse problems
Terrence Alsup, Benjamin Peherstorfer
Multi-fidelity methods leverage low-cost surrogate models to speed up computations and make occasional recourse to expensive high-fidelity models to establish accuracy guarantees.…