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 tranfer 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.

翻译：我们提出一种用于模拟辐射传输的新型机器学习算法。该算法基于物理信息神经网络（PINNs），通过最小化底层辐射传输方程的残差进行训练。我们通过大量实验与理论误差估计证明，PINNs提供了易于实现、快速、稳健且精确的辐射传输模拟方法。此外，我们还提出了一种基于PINN的算法，用于高效模拟辐射传输逆问题。