Use of physics-augmented neural networks for unsupervised learning of material constitutive relations - Comparison of the NN-Euclid and NN-mCRE methods

Summary

This is a publication. If there is no link to the publication on this page, you can try the pre-formated search via the search engines listed on this page.

Authors: E. ZEMBRA, A. BENADY, E. BARANGER, L. CHAMOIN

Journal publisher: ENS Paris-Saclay

Published year: 2023