Mixed formulation of physics‐informed neural networks for thermo‐mechanically coupled systems and heterogeneous domains

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 engineerin...

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