Physics informed neural networks for simulating radiative transfer
Siddhartha Mishra, Roberto Molinaro
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 transfer 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.
Topics & Concepts
Radiative transferArtificial neural networkAtmospheric radiative transfer codesComputer scienceResidualTransfer of learningInverse problemPhysicsAlgorithmStatistical physicsApplied mathematicsArtificial intelligenceMathematicsOpticsMathematical analysisModel Reduction and Neural NetworksNuclear reactor physics and engineeringFluid Dynamics and Turbulent Flows