Multiscale modeling of materials and neural operators

Fuente: arXiv
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Autor principal: Bhattacharya, Kaushik
Formato: Preprint
Publicado: 2026
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author Bhattacharya, Kaushik
author_facet Bhattacharya, Kaushik
contents Multiscale modeling is essential for understanding the complex behavior of materials. However, accurately transferring all relevant information from one scale to another has remained an outstanding challenge. Neural operators, discretization-independent generalizations of neural networks, is proving to be a powerful tool in addressing this challenge. This article provides an introduction to neural operators, and illustrates their use in multiscale modeling of materials through three selected examples.
format Preprint
id arxiv_https___arxiv_org_abs_2605_08466
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Multiscale modeling of materials and neural operators
Bhattacharya, Kaushik
Materials Science
Multiscale modeling is essential for understanding the complex behavior of materials. However, accurately transferring all relevant information from one scale to another has remained an outstanding challenge. Neural operators, discretization-independent generalizations of neural networks, is proving to be a powerful tool in addressing this challenge. This article provides an introduction to neural operators, and illustrates their use in multiscale modeling of materials through three selected examples.
title Multiscale modeling of materials and neural operators
topic Materials Science
url https://arxiv.org/abs/2605.08466