FPL-net: A deep learning framework for solving the nonlinear Fokker–Planck–Landau collision operator for anisotropic temperature relaxation
Year: 2024
Type: article
Abstract: The nonlinear collision operator consumes a significant amount of computation time in tokamak whole-volume modeling, and in current numerical methods , the computational time grows O ( n 2 ) , with n representing the number of plasma species. In this study, we address the acceleration of the Fokker–Planck–Landau (FPL) collision operator using deep learning techniques. The developed FPL-net, a deep learning-based nonlinear Fokker–Planck–Landau collision operator, is a fully convolutional neural network optimized for computational speed with a compact model structure. FPL-net was trained on data representing various temperature conditions of an electron plasma on a two-dimensional velocity grid, ensuring generality. The network's training incorporated physics-informed loss functions for density, momentum, and energy moments of the plasma probability distribution function , which served as constraints, and it was trained to recursively predict two time steps, achieving robust accuracy. Notably, FPL-net demonstrated full temperature relaxation, representing the first time this has been accomplished by a deep learning–based FPL collision operator. Additional experiments with noisy inputs and extended rollouts validated the model's accuracy, which also shows over 1000x acceleration compared to traditional finite volume methods . We discuss the achieved acceleration through deep learning techniques and propose potential avenues for further enhancement and refinement in future research.
Source: Journal of Computational Physics
Authors: Hyeongjun Noh, Jimin Lee, Eisung Yoon
Institution: Ulsan National Institute of Science and Technology
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,860
OpenAlex ID: W4405285010