Physics-Informed Neural Networks (PINN) are neural networks that encode the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network training . PINNs have emerged as an essential tool to solve various challenging problems, such as computing linear and non-linear PDEs, completing data assimilation and uncertainty quantification tasks .
<clarity> Physics-Informed Neural Networks (PINN) are neural networks that encode the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network training . PINNs have emerged as an essential tool to solve various challenging problems, such as computing linear and non-linear PDEs, completing data assimilation and uncertainty quantification tasks .
Physics-Informed Neural Networks (PINN) are neural networks encoding the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network training . PINNs have emerged as an essential tool to solve various challenging problems, such as computing linear and non-linear PDEs, completing data assimilation and uncertainty quantification tasks .