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In recent years, Physics-Informed Neural Networks[1] have been applied to various types of application tasks. This example shows how to train a neural network to predict temperature distributions given new initial and boundary conditions. The neural network was trained using a loss function that includes a data loss component, which measures the discrepancy between the network's predictions and targets derived from finite element simulations, as well as a physics-informed loss component that evaluates the residual of the governing partial differential equation (PDE).
The PDE used in the loss function is the transient heat equation:
To get started, clone this repository and run "Example_pinn.mlx".
MATLAB version should be R2024a and later (Tested in R2024a)
[1] Raissi, Maziar, Paris Perdikaris, and George E. Karniadakis. "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations." Journal of Computational Physics 378 (2019): 686-707.
The license is available in license.txt file in this GitHub repository.
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