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.

翻译：我们提议以物理信息神经网络作为生物样品成像学重建的前方模型,我们通过将这个网络与赫尔莫赫茨等式培训为物理损失来证明,我们可以准确地预测分散的场域,可以证明一个预先训练的网络可以对不同的样品进行微调,并比其他数字解决方案更快地用于解决散布问题。我们用数字和实验结果来评估我们的方法。我们的物理信息神经网络可以针对任何前向和反向散布问题加以普及。