1 citations · 2 across the 4 of their papers we have counts for
5 papers
Nonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemes
Paul Schwerdtner, Philipp Schulze, Jules Berman +1
This work focuses on the conservation of quantities such as Hamiltonians, mass, and momentum when solution fields of partial differential equations are approximated with nonlinear…
Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks
Jules Berman, Benjamin Peherstorfer
Training neural networks sequentially in time to approximate solution fields of time-dependent partial differential equations can be beneficial for preserving causality and other p…
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…
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…