Physics Informed Neural Networks for Simulating Radiative Transfer
This work addresses radiative transfer simulation, a domain-specific problem in physics and engineering, with incremental improvements in method application.
The authors tackled the simulation of radiative transfer by proposing a physics informed neural network (PINN) algorithm, achieving a method that is easy to implement, fast, robust, and accurate, with theoretical error estimates and efficient handling of inverse problems.
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.