论文标题
使用物理知情的神经网络进行超新星辐射转移模拟
Using Physics Informed Neural Networks for Supernova Radiative Transfer Simulation
论文作者
论文摘要
我们使用物理知情的神经网络（PINN）来求解辐射转移方程，并计算IA型超新星（SN〜IA）SN 2011FE的合成光谱。该计算基于局部热力学平衡（LTE），其中包括9个元素。包括的物理过程包括近似辐射平衡，结合结合的跃迁和多普勒效应。基于PINN的伽马射线散射近似用于放射性衰减能量沉积。将PINN合成光谱与观察到的光谱，由Monte-Carlo辐射转移程序TARDIS计算得出的合成光谱以及辐射转移方程的形式解决方案。我们讨论了这种基于深度学习的辐射传递方程求解器的挑战和潜力。实际上，Pinns为整个时空提供了同时解决辐射场和热状态的大气问题的前景。我们已经采取了适度的步骤来实现我们的计算，这需要许多近似值才能在Pinn Altiresere Solutions的开发中可行。
We use physics informed neural networks (PINNs) to solve the radiative transfer equation and calculate a synthetic spectrum for a Type Ia supernova (SN~Ia) SN 2011fe. The calculation is based on local thermodynamic equilibrium (LTE) and 9 elements are included. Physical processes included are approximate radiative equilibrium, bound-bound transitions, and the Doppler effect. A PINN based gamma-ray scattering approximation is used for radioactive decay energy deposition. The PINN synthetic spectrum is compared to an observed spectrum, a synthetic spectrum calculated by the Monte-Carlo radiative transfer program TARDIS, and the formal solution of the radiative transfer equation. We discuss the challenges and potential of this deep-learning based radiative transfer equation solver. In fact, PINNs offer the prospect of simultaneous solution of the atmosphere problem for both radiation field and thermal state throughout spacetime. We have made modest steps to realizing that prospect with our calculations which required many approximations in order to be feasible at this point in the development of PINN atmosphere solutions.
