A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method
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Publication:2096848
DOI10.1016/j.cma.2022.115616OpenAlexW4296468671MaRDI QIDQ2096848
Ahmad Moeineddin, Ali Harandi, Shahed Rezaei, Stefanie Reese, Bai-Xiang Xu
Publication date: 11 November 2022
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2206.13103
Related Items (10)
Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity ⋮ On the order of derivation in the training of physics-informed neural networks: case studies for non-uniform beam structures ⋮ Solving Elliptic Problems with Singular Sources Using Singularity Splitting Deep Ritz Method ⋮ Model-driven identification framework for optimal constitutive modeling from kinematics and rheological arrangement ⋮ Discovering the mechanics of artificial and real meat ⋮ Physics-informed radial basis network (PIRBN): a local approximating neural network for solving nonlinear partial differential equations ⋮ On automated model discovery and a universal material subroutine for hyperelastic materials ⋮ Adversarial deep energy method for solving saddle point problems involving dielectric elastomers ⋮ Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios ⋮ Deep energy method in topology optimization applications
Uses Software
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