Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation

TL;DR

This paper introduces a physics-informed neural network approach based on the Boltzmann-BGK formulation to effectively solve both forward and inverse flow problems across continuum and rarefied regimes, demonstrating high accuracy and improved training efficiency.

Contribution

The study develops a novel PINN framework with three sub-networks for modeling Boltzmann-BGK equations, enabling accurate flow predictions and inverse inference in complex regimes.

Findings

Successfully solves benchmark flows up to Knudsen number 5

Accurately infers velocity fields with limited interior data

Achieves three-fold training speedup using transfer learning

Abstract

In this study, we employ physics-informed neural networks (PINNs) to solve forward and inverse problems via the Boltzmann-BGK formulation (PINN-BGK), enabling PINNs to model flows in both the continuum and rarefied regimes. In particular, the PINN-BGK is composed of three sub-networks, i.e., the first for approximating the equilibrium distribution function, the second for approximating the non-equilibrium distribution function, and the third one for encoding the Boltzmann-BGK equation as well as the corresponding boundary/initial conditions. By minimizing the residuals of the governing equations and the mismatch between the predicted and provided boundary/initial conditions, we can approximate the Boltzmann-BGK equation for both continuous and rarefied flows. For forward problems, the PINN-BGK is utilized to solve various benchmark flows given boundary/initial conditions, e.g., Kovasznay flow, Taylor-Green flow, cavity flow, and micro Couette flow for Knudsen number up to 5. For inverse problems, we focus on rarefied flows in which accurate boundary conditions are difficult to obtain. We employ the PINN-BGK to infer the flow field in the entire computational domain given a limited number of interior scattered measurements on the velocity with unknown boundary conditions. Results for the two-dimensional micro Couette and micro cavity flows with Knudsen numbers ranging from 0.1 to 10 indicate that the PINN-BGK can infer the velocity field in the entire domain with good accuracy. Finally, we also present some results on using transfer learning to accelerate the training process. Specifically, we can obtain a three-fold speedup compared to the standard training process (e.g., Adam plus L-BFGS-B) for the two-dimensional flow problems considered in our work.