Analyses of internal structures and defects in materials using physics-informed neural networks

Characterizing internal structures and defects in materials is a challenging task, often requiring solutions to inverse problems with unknown topology, geometry, material properties, and nonlinear deformation. Here, we present a general framework based on physics-informed neural networks for identif...

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Detalles Bibliográficos
Autores principales: Zhang, Enrui, Dao, Ming, Karniadakis, George Em, Suresh, Subra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
Materias:
Physical and Materials Sciences
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8849303/
https://www.ncbi.nlm.nih.gov/pubmed/35171670
http://dx.doi.org/10.1126/sciadv.abk0644

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8849303/
https://www.ncbi.nlm.nih.gov/pubmed/35171670
http://dx.doi.org/10.1126/sciadv.abk0644

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