Mixed formulation of physics‐informed neural networks for thermo‐mechanically coupled systems and heterogeneous domains
Mixed formulation of physics‐informed neural networks for thermo‐mechanically coupled systems and heterogeneous domains
Abstract Physics‐informed neural networks (PINNs) are a new tool for solving boundary value problems by defining loss functions of neural networks based on governing equations, boundary conditions, and initial conditions. Recent investigations have shown that when designing loss functions for many engineering problems, using first‐order derivatives and combining equations from …