par Biswal, Pratibha 
;Avdijaj, Jetnis ;Parente, Alessandro ;Coussement, Axel
Référence Journal of Fluid Flow, Heat and Mass Transfer, 11, page (356-362)
Publication Publié, 2024-10-01
;Avdijaj, Jetnis ;Parente, Alessandro ;Coussement, Axel Référence Journal of Fluid Flow, Heat and Mass Transfer, 11, page (356-362)
Publication Publié, 2024-10-01
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 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. |
