Cargando…
Predicting the Non-Deterministic Response of a Micro-Scale Mechanical Model Using Generative Adversarial Networks
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 ma...
Autores principales: | Argilaga, Albert, Zhuang, Duanyang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838419/ https://www.ncbi.nlm.nih.gov/pubmed/35160911 http://dx.doi.org/10.3390/ma15030965 |
Ejemplares similares
-
Generative Adversarial Networks for Crystal Structure
Prediction
por: Kim, Sungwon, et al.
Publicado: (2020) -
FEM-GAN: A Physics-Supervised Deep Learning Generative Model for Elastic Porous Materials
por: Argilaga, Albert
Publicado: (2023) -
Generating mobility networks with generative adversarial networks
por: Mauro, Giovanni, et al.
Publicado: (2022) -
Generative adversarial networks for image generation
por: Mao, Xudong, et al.
Publicado: (2021) -
Hands-on generative adversarial networks with Keras: your guide to implementing next-generation generative adversarial networks
por: Valle, Rafael
Publicado: (2019)