4 citations · 9 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
Active operator inference for learning low-dimensional dynamical-system models from noisy data
Wayne Isaac Tan Uy, Yuepeng Wang, Yuxiao Wen +1
Noise poses a challenge for learning dynamical-system models because already small variations can distort the dynamics described by trajectory data. This work builds on operator in…
Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories
Wayne Isaac Tan Uy, Benjamin Peherstorfer
This work introduces a non-intrusive model reduction approach for learning reduced models from partially observed state trajectories of high-dimensional dynamical systems. The prop…
Probabilistic error estimation for non-intrusive reduced models learned from data of systems governed by linear parabolic partial differential equations
Wayne Isaac Tan Uy, Benjamin Peherstorfer
This work derives a residual-based a posteriori error estimator for reduced models learned with non-intrusive model reduction from data of high-dimensional systems governed by line…
Neural network representation of the probability density function of diffusion processes
Wayne Isaac Tan Uy, Mircea Grigoriu
Physics-informed neural networks are developed to characterize the state of dynamical systems in a random environment. The neural network approximates the probability density funct…
Specification of additional information for solving stochastic inverse problems
Wayne Isaac T. Uy, Mircea D. Grigoriu
Methods have been developed to identify the probability distribution of a random vector from information consisting of its bounded range and the probability density function or…