11 citations · 20 across the 10 of their papers we have counts for
16 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…
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…
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…
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.…