Solving forward and inverse problems of contact mechanics using physics-informed neural networks
Abstract This paper explores the ability of physics-informed neural networks (PINNs) to solve forward and inverse problems of contact mechanics for small deformation elasticity. We deploy PINNs in a mixed-variable formulation enhanced by output transformation to enforce Dirichlet and Neumann boundar...
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| Principais autores: | , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
SpringerOpen
2024-05-01
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| Series: | Advanced Modeling and Simulation in Engineering Sciences |
| Assuntos: | |
| Acceso en liña: | https://doi.org/10.1186/s40323-024-00265-3 |
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