opPINN: Physics-Informed Neural Network with operator learning to approximate solutions to the Fokker-Planck-Landau equation

TL;DR

This paper introduces opPINN, a hybrid neural network framework combining physics-informed learning and operator surrogates to efficiently approximate solutions to the complex Fokker-Planck-Landau equation in multiple dimensions.

Contribution

The novel opPINN framework integrates operator learning with PINNs, significantly reducing computational costs for solving the FPL equation and enabling solutions under various conditions.

Findings

Operator surrogate models accelerate PINN computations.

opPINN provides accurate neural network solutions for FPL in 2D and 3D.

The neural network solution converges to the classical solution as training progresses.

Abstract

We propose a hybrid framework opPINN: physics-informed neural network (PINN) with operator learning for approximating the solution to the Fokker-Planck-Landau (FPL) equation. The opPINN framework is divided into two steps: Step 1 and Step 2. After the operator surrogate models are trained during Step 1, PINN can effectively approximate the solution to the FPL equation during Step 2 by using the pre-trained surrogate models. The operator surrogate models greatly reduce the computational cost and boost PINN by approximating the complex Landau collision integral in the FPL equation. The operator surrogate models can also be combined with the traditional numerical schemes. It provides a high efficiency in computational time when the number of velocity modes becomes larger. Using the opPINN framework, we provide the neural network solutions for the FPL equation under the various types of initial conditions, and interaction models in two and three dimensions. Furthermore, based on the theoretical properties of the FPL equation, we show that the approximated neural network solution converges to the a priori classical solution of the FPL equation as the pre-defined loss function is reduced.