Physics Informed Neural Networks for Simulating Radiative Transfer

TL;DR

This paper introduces physics-informed neural networks (PINNs) for simulating radiative transfer, demonstrating their accuracy, robustness, and efficiency through extensive experiments and theoretical analysis.

Contribution

The paper presents a novel PINN-based algorithm specifically designed for radiative transfer simulation and inverse problem solving, with theoretical error estimates and extensive validation.

Findings

PINNs are easy to implement and fast for radiative transfer simulations.

PINNs provide robust and accurate results validated by experiments.

The method efficiently solves inverse radiative transfer problems.

Abstract

We propose a novel machine learning algorithm for simulating radiative transfer. Our algorithm is based on physics informed neural networks (PINNs), which are trained by minimizing the residual of the underlying radiative tranfer equations. We present extensive experiments and theoretical error estimates to demonstrate that PINNs provide a very easy to implement, fast, robust and accurate method for simulating radiative transfer. We also present a PINN based algorithm for simulating inverse problems for radiative transfer efficiently.