Work

Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical Simulations

Year: 2023

Type: preprint

Abstract: Traditional partial differential equation (PDE) solvers can be computationally expensive, which motivates the development of faster methods, such as reduced-order-models (ROMs). We present GPLaSDI, a hybrid deep-learning and Bayesian ROM. GPLaSDI trains an autoencoder on full-order-model (FOM) data and simultaneously learns simpler equations governing the latent space. These equations are interpolated with Gaussian Processes, allowing for uncertainty quantification and active learning, even with limited access to the FOM solver. Our framework is able to achieve up to 100,000 times speed-up and less than 7% relative error on fluid mechanics problems.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2312.01021

Authors: Bonneville, Christophe, Choi, Youngsoo, Ghosh, Debojyoti, Belof, Jonathan L.

Institutions: Cornell University, Lawrence Livermore National Laboratory, Sandia National Laboratories

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: U.S. Department of Energy, Office of Science, National Nuclear Security Administration, Advanced Scientific Computing Research, Laboratory Directed Research and Development, Lawrence Livermore National Laboratory

Awards: G1356433355, G14554683, G2145270357, G217780377, G3390444915, G4094752214, G4744444288, CENTER FOR HIERARCHICAL AND ROBUST MODELING OF NON-EQUILIBRIUM TRANSPORT (CHARMNET), G5685548091, G574795485, G6829254754, G6904510850, G8397015973, G991281326

OpenAlex ID: W4389363324

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Cited by: 1

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