Synthesizing controlled microstructures of porous media using generative adversarial networks and reinforcement learning

For material modeling and discovery, synthetic microstructures play a critical role as digital twins. They provide stochastic samples upon which direct numerical simulations can be conducted to populate material databases. A large ensemble of simulation data on synthetic microstructures may provide...

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Detalles Bibliográficos
Autores principales: Nguyen, Phong C. H., Vlassis, Nikolaos N., Bahmani, Bahador, Sun, WaiChing, Udaykumar, H. S., Baek, Stephen S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9156766/
https://www.ncbi.nlm.nih.gov/pubmed/35641549
http://dx.doi.org/10.1038/s41598-022-12845-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9156766/
https://www.ncbi.nlm.nih.gov/pubmed/35641549
http://dx.doi.org/10.1038/s41598-022-12845-7

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