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04/10/2023
Learning solution of nonlinear constitutive material models using physics-informed neural networks: COMM-PINN
We applied physics-informed neural networks to solve the constitutive re...
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02/09/2023
Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains
Deep learning methods find a solution to a boundary value problem by def...
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06/27/2022
A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method
Physics-informed neural networks (PINNs) are capable of finding the solu...
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08/28/2021
An anisotropic cohesive fracture model: advantages and limitations of length-scale insensitive phase-field damage models
The goal of the current work is to explore direction-dependent damage in...
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08/05/2021