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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Bibliografiska uppgifter

Huvudupphov:	Tarik Sahin, Max von Danwitz, Alexander Popp
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	SpringerOpen 2024-05-01
Serie:	Advanced Modeling and Simulation in Engineering Sciences
Ämnen:	Physics-informed neural networks Mixed-variable formulation Contact mechanics Enforcing inequalities Fischer–Burmeister NCP-function
Länkar:	https://doi.org/10.1186/s40323-024-00265-3
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