Physics-Informed Neural Network Method for Solving One-Dimensional Advection Equation Using PyTorch
This addresses computational uncertainty in physics simulations for researchers, but it is incremental as it applies an existing PINN method to a specific equation.
The paper tackled the problem of numerical inaccuracies in solving the one-dimensional advection equation by comparing finite-difference methods with physics-informed neural networks (PINNs), finding that only PINNs accurately predicted outcomes without oscillations.
Numerical solutions to the equation for advection are determined using different finite-difference approximations and physics-informed neural networks (PINNs) under conditions that allow an analytical solution. Their accuracy is examined by comparing them to the analytical solution. We used a machine learning framework like PyTorch to implement PINNs. PINNs approach allows training neural networks while respecting the PDEs as a strong constraint in the optimization as apposed to making them part of the loss function. In standard small-scale circulation simulations, it is shown that the conventional approach incorporates a pseudo diffusive effect that is almost as large as the effect of the turbulent diffusion model; hence the numerical solution is rendered inconsistent with the PDEs. This oscillation causes inaccuracy and computational uncertainty. Of all the schemes tested, only the PINNs approximation accurately predicted the outcome. We assume that the PINNs approach can transform the physics simulation area by allowing real-time physics simulation and geometry optimization without costly and time-consuming simulations on large supercomputers.