Physics Informed Neural Networks for Simulating Radiative Transfer

Code

• github.com/mroberto166/PinnsSub
  OfficialIn paperpytorch★ 73
• github.com/DavidDahlbudding/AtmosphericScatteringPinn
  pytorch★ 3

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.

Tasks

Reproductions