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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 ...
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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...
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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...
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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...
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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...
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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...
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01/15/2020