论文标题
物理知识的神经网络用于模拟辐射转移
Physics Informed Neural Networks for Simulating Radiative Transfer
论文作者
论文摘要
我们提出了一种新型的机器学习算法，用于模拟辐射转移。我们的算法基于物理知情的神经网络（PINN），这些神经网络是通过最大程度地减少基础辐射转速方程的残留而训练的。我们提出了广泛的实验和理论误差估计，以证明PINN提供了一种非常易于实现，快速，健壮和准确的方法来模拟辐射传递。我们还提出了一种基于PINN的算法，以有效地模拟辐射转移的逆问题。
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.
