research
∙
07/27/2023
Lookahead data-gathering strategies for online adaptive model reduction of transport-dominated problems
Online adaptive model reduction efficiently reduces numerical models of ...
share
research
∙
07/23/2023
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices
We introduce a multifidelity estimator of covariance matrices formulated...
research
∙
06/27/2023
Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes
Training nonlinear parametrizations such as deep neural networks to nume...
research
∙
02/19/2023
Rank-Minimizing and Structured Model Inference
While extracting information from data with machine learning plays an in...
research
∙
01/31/2023
Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry
We introduce a multi-fidelity estimator of covariance matrices that empl...
research
∙
01/18/2023
Meta variance reduction for Monte Carlo estimation of energetic particle confinement during stellarator optimization
This work introduces meta estimators that combine multiple multifidelity...
research
∙
12/06/2022
Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models
Multilevel Stein variational gradient descent is a method for particle-b...
research
∙
12/02/2022
Operator inference with roll outs for learning reduced models from scarce and low-quality data
Data-driven modeling has become a key building block in computational sc...
research
∙
11/20/2022
Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification
Multi-fidelity Monte Carlo methods leverage low-fidelity and surrogate m...
research
∙
09/14/2022
Reduced models with nonlinear approximations of latent dynamics for model premixed flame problems
Efficiently reducing models of chemically reacting flows is often challe...
research
∙
07/22/2022
Context-aware controller inference for stabilizing dynamical systems from scarce data
This work introduces a data-driven control approach for stabilizing high...
research
∙
05/30/2022
Multi-fidelity robust controller design with gradient sampling
Robust controllers that stabilize dynamical systems even under disturban...
research
∙
03/02/2022
Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations
Machine learning methods have been shown to give accurate predictions in...
research
∙
02/28/2022
On the sample complexity of stabilizing linear dynamical systems from data
Learning controllers from data for stabilizing dynamical systems typical...
research
∙
08/13/2021
Accelerating the estimation of energetic particle confinement statistics in stellarators using multifidelity Monte Carlo
In the design of stellarators, energetic particle confinement is a criti...
research
∙
07/20/2021
Active operator inference for learning low-dimensional dynamical-system models from noisy data
Noise poses a challenge for learning dynamical-system models because alr...
research
∙
07/06/2021
Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference
Operator inference learns low-dimensional dynamical-system models with p...
research
∙
04/05/2021
Multilevel Stein variational gradient descent with applications to Bayesian inverse problems
This work presents a multilevel variant of Stein variational gradient de...
research
∙
03/01/2021
Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories
This work introduces a non-intrusive model reduction approach for learni...
research
∙
10/22/2020
Context-aware surrogate modeling for balancing approximation and sampling costs in multi-fidelity importance sampling and Bayesian inverse problems
Multi-fidelity methods leverage low-cost surrogate models to speed up co...
research
∙
07/28/2020
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems
Classical reduced models are low-rank approximations using a fixed basis...
research
∙
05/12/2020
Probabilistic error estimation for non-intrusive reduced models learned from data of systems governed by linear parabolic partial differential equations
This work derives a residual-based a posteriori error estimator for redu...
research
∙
02/22/2020
Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms
This work presents a non-intrusive model reduction method to learn low-d...
research
∙
12/30/2019
Manifold Approximations via Transported Subspaces: Model reduction for transport-dominated problems
This work presents a method for constructing online-efficient reduced mo...
research
∙
12/17/2019
Lift Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
We present Lift Learn, a physics-informed method for learning low-di...
research
∙
09/30/2019
Learning low-dimensional dynamical-system models from noisy frequency-response data with Loewner rational interpolation
Loewner rational interpolation provides a versatile tool to learn low-di...
research
∙
08/29/2019
Sampling low-dimensional Markovian dynamics for pre-asymptotically recovering reduced models from data with operator inference
This work introduces a method for learning low-dimensional models from d...
research
∙
05/07/2019
Multifidelity probability estimation via fusion of estimators
This paper develops a multifidelity method that enables estimation of fa...
research
∙
12/05/2018
Model reduction for transport-dominated problems via online adaptive bases and adaptive sampling
This work presents a model reduction approach for problems with coherent...
research
∙
08/28/2018
A transport-based multifidelity preconditioner for Markov chain Monte Carlo
Markov chain Monte Carlo (MCMC) sampling of posterior distributions aris...
research
∙
06/28/2018