论文标题
用于衍射断层扫描的物理信息神经网络
Physics-informed neural networks for diffraction tomography
论文作者
论文摘要
我们提出了一个具有物理信息的神经网络，作为生物样品的层析成像重建的正向模型。我们证明，通过用Helmholtz方程训练该网络作为物理损失，我们可以准确预测散射的场。可以证明，与其他数值解决方案相比，可以对不同样本进行微调的网络进行微调，并用于解决散射问题的速度要快得多。我们通过数值和实验结果评估我们的方法。我们的物理知识神经网络可以推广到任何前进和反向散射问题。
We propose a physics-informed neural network as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmholtz equation as a physical loss, we can predict the scattered field accurately. It will be shown that a pretrained network can be fine-tuned for different samples and used for solving the scattering problem much faster than other numerical solutions. We evaluate our methodology with numerical and experimental results. Our physics-informed neural networks can be generalized for any forward and inverse scattering problem.
