Physics Informed Neural Networks for Simulating Radiative Transfer

• URL: http://arxiv.org/abs/2009.13291v3
• Date: Wed, 6 Dec 2023 09:16:26 GMT
• Title: Physics Informed Neural Networks for Simulating Radiative Transfer
• Authors: Siddhartha Mishra and Roberto Molinaro
• Abstract summary: 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.
• Score: 16.758334184623152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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