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Physics Informed Neural Networks for Simulating Radiative Transfer

by S. Mishra and R. Molinaro

(Report number 2020-62)

Abstract
We propose a novel machine learning algorithm for simulating radiative transfer. Our algorithmis based on physics informed neural networks (PINNs), which are trained by minimizing the residualof the underlying radiative tranfer equations. We present extensive experiments and theoretical errorestimates to demonstrate that PINNs provide a very easy to implement, fast, robust and accuratemethod for simulating radiative transfer. We also present a PINN based algorithm for simulatinginverse problems for radiative transfer efficiently.

Keywords: Deep Learning Physic Informed Neural Networks Radiative Transfer

@Techreport{MM20_935,
  author = {S. Mishra and R. Molinaro},
  title = {Physics Informed Neural Networks for Simulating Radiative Transfer},
  institution = {Seminar for Applied Mathematics, ETH Z{\"u}rich},
  number = {2020-62},
  address = {Switzerland},
  url = {https://www.sam.math.ethz.ch/sam_reports/reports_final/reports2020/2020-62.pdf },
  year = {2020}
}

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