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Found 4 papers, 3 papers with code
A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformations
Our method, inspired by operator learning and the finite element method, demonstrates the ability to train without relying on data from other numerical solvers.
Learning solution of nonlinear constitutive material models using physics-informed neural networks: COMM-PINN
In order to demonstrate the applicability of the methodology in handling complex path dependency in a three-dimensional (3D) scenario, we tested the approach using the equations governing a damage model for a three-dimensional interface model.
Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains
In this work, we propose applying the mixed formulation to solve multi-physical problems, specifically a stationary thermo-mechanically coupled system of equations.
A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method
Later on, the strong form which has a higher order of derivatives is applied to the spatial gradients of the primary variable as the physical constraint.
