par Biswal, Pratibha 
;Avdijaj, Jetnis ;Parente, Alessandro ;Coussement, Axel
Référence Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering
Publication Publié, 2024-11-01
;Avdijaj, Jetnis ;Parente, Alessandro ;Coussement, Axel Référence Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering
Publication Publié, 2024-11-01
Article révisé par les pairs
| Résumé : | 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 physicsinformed 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 RTE solution. The results from PINN model are validated against results from previous studies. Findings for various 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. |
