Pratibha Biswal, Jetnis Avdijaj, Alessandro Parente, Axel Coussement

Abstract: The radiative transfer equation (RTE) serves as a fundamental framework for modeling the propagation of electromagnetic waves through a medium. Traditionally, solving the RTE has been challenging and computationally intensive. In this work, a physics-informed neural network (PINN) model is used to solve the 1D radiative transfer equation. The PINN approach integrates physical laws into the neural network training process, offering a novel way to address the computational complexities of the RTE solution. The results from the PINN model are validated against results from previous studies. Findings for different extinction coefficient are presented demonstrating the efficacy and accuracy of the PINN approach. This work contributes to the theoretical understanding of the RTE and highlights the potential of PINNs to enhance and streamline numerical methods in this domain.

Keywords: Thermal Radiation, Radiation Transfer Equation (RTE), Scattering, Participating media, Physics-informed neural network (PINN), artificial neural network (ANN)

Date Published: October 1, 2024
DOI: 10.11159/jffhmt.2024.035

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