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