Training Deep Surrogate Models with Large Scale Online Learning - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2023

Training Deep Surrogate Models with Large Scale Online Learning

Abstract

The spatiotemporal resolution of Partial Differential Equations (PDEs) plays important roles in the mathematical description of the world's physical phenomena. In general, scientists and engineers solve PDEs numerically by the use of computationally demanding solvers. Recently, deep learning algorithms have emerged as a viable alternative for obtaining fast solutions for PDEs. Models are usually trained on synthetic data generated by solvers, stored on disk and read back for training. This paper advocates that relying on a traditional static dataset to train these models does not allow the full benefit of the solver to be used as a data generator. It proposes an open source online training framework for deep surrogate models. The framework implements several levels of parallelism focused on simultaneously generating numerical simulations and training deep neural networks. This approach suppresses the I/O and storage bottleneck associated with disk-loaded datasets, and opens the way to training on significantly larger datasets. Experiments compare the offline and online training of four surrogate models, including state-of-the-art architectures. Results indicate that exposing deep surrogate models to more dataset diversity, up to hundreds of GB, can increase model generalization capabilities. Fully connected neural networks, Fourier Neural Operator (FNO), and Message Passing PDE Solver prediction accuracy is improved by 68%, 16% and 7%, respectively.
Fichier principal
Vignette du fichier
main.pdf (2.21 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04102400 , version 1 (28-06-2023)

Licence

Identifiers

Cite

Lucas Meyer, Marc Schouler, Robert Alexander Caulk, Alejandro Ribés, Bruno Raffin. Training Deep Surrogate Models with Large Scale Online Learning. International Conference on Machine Learning, Jul 2023, Honolulu (Hawai'i), United States. pp.24614-24630. ⟨hal-04102400⟩
180 View
52 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More