270 citations · 618 across the 36 of their papers we have counts for
6 papers · 1 filter
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…