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：我们建议使用新型机器学习算法模拟辐射传输。我们的算法基于物理知情神经网络(PINNs),这些网络通过最大限度地减少潜在辐射变异方程式的剩余部分来接受培训。我们提出了广泛的实验和理论错误估计,以证明PINNs为模拟辐射传输提供了非常容易实施、快速、稳健和准确的方法。我们还提出了基于物理知情神经网络(PINNs)的算法,以模拟反向问题,从而有效进行辐射转移。