论文标题
部分可观测时空混沌系统的无模型预测
A physics-defined recurrent neural network to compute coherent light wave scattering on the millimetre scale
论文作者
论文摘要
诸如生物组织之类的异质材料以随机但确定性的方式散布光。波前塑形可以扭转散射的影响以实现深部组织显微镜。这样的方法需要侵入性访问内部字段或数值计算它的能力。但是，在与显微镜相关的比例尺上计算相干字段仍然对消费者硬件的要求过高。在这里，我们展示了反复的神经网络如何在不训练的情况下镜像麦克斯韦的方程式。 By harnessing public machine learning infrastructure, such \emph{Scattering Network} can compute the $633\;\textrm{nm}$-wavelength light field throughout a $25\;\textrm{mm}^2$ or $176^3\;μ\textrm{m}^3$ scattering volume.消除训练阶段将计算时间缩短至最低限度，重要的是，它确保了完全确定性的解决方案，没有任何训练偏见。与开源电磁求解器的集成使任何具有Internet连接的研究人员都可以计算出复杂的光散射，这些散落的体积大于两个数量级。
Heterogeneous materials such as biological tissue scatter light in random, yet deterministic, ways. Wavefront shaping can reverse the effects of scattering to enable deep-tissue microscopy. Such methods require either invasive access to the internal field or the ability to numerically compute it. However, calculating the coherent field on a scale relevant to microscopy remains excessively demanding for consumer hardware. Here we show how a recurrent neural network can mirror Maxwell's equations without training. By harnessing public machine learning infrastructure, such \emph{Scattering Network} can compute the $633\;\textrm{nm}$-wavelength light field throughout a $25\;\textrm{mm}^2$ or $176^3\;μ\textrm{m}^3$ scattering volume. The elimination of the training phase cuts the calculation time to a minimum and, importantly, it ensures a fully deterministic solution, free of any training bias. The integration with an open-source electromagnetic solver enables any researcher with an internet connection to calculate complex light-scattering in volumes that are larger by two orders of magnitude
