Predicting the Non-Deterministic Response of a Micro-Scale Mechanical Model Using Generative Adversarial Networks.

ABSTRACT: Recent improvements in micro-scale material descriptions allow to build increasingly refined multiscale models in geomechanics. This often comes at the expense of computational cost which can eventually become prohibitive. Among other characteristics, the non-determinism of a micro-scale response makes its replacement by a surrogate particularly challenging. Machine Learning (ML) is a promising technique to substitute physics-based models, nevertheless existing ML algorithms for the prediction of material response do not integrate non-determinism in the learning process. Is it possible to use the numerical output of the latest micro-scale descriptions to train a ML algorithm that will then provide a response at a much lower computational cost? A series of ML algorithms with different level

SUBMITTER: Argilaga A 

PROVIDER: S-EPMC8838419 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature