A Taylor Based Sampling Scheme for Machine Learning in Computational Physics - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Other Publications Year : 2019

A Taylor Based Sampling Scheme for Machine Learning in Computational Physics

Abstract

Machine Learning (ML) is increasingly used to construct surrogate models for physical simulations. We take advantage of the ability to generate data using numerical simulations programs to train ML models better and achieve accuracy gain with no performance cost. We elaborate a new data sampling scheme based on Taylor approximation to reduce the error of a Deep Neural Network (DNN) when learning the solution of an ordinary differential equations (ODE) system.
Fichier principal
Vignette du fichier
NeurIPS_2019_workshop_final.pdf (530.2 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03114984 , version 1 (20-01-2021)
hal-03114984 , version 2 (28-01-2021)

Identifiers

Cite

Paul Novello, Gaël Poëtte, David Lugato, Pietro Marco Congedo. A Taylor Based Sampling Scheme for Machine Learning in Computational Physics. 2019. ⟨hal-03114984v2⟩
129 View
81 Download

Altmetric

Share

Gmail Facebook X LinkedIn More