通过物理感知深度学习实现实时三维纳米尺度相干成像
Real-time 3D Nanoscale Coherent Imaging via Physics-aware Deep Learning
AI总结：
本文提出物理感知深度学习框架3D-CDI-NN，利用三维X射线相干散射数据实时反演纳米尺度结构与应变，速度比传统迭代相位恢复方法快数百倍且精度损失可忽略。
摘要：
相位恢复，即仅从测量强度中恢复丢失相位信息的问题，是一个在从天文学到纳米尺度成像的各种成像模态中广泛面临的逆问题。当前的相位恢复过程本质上是迭代的。因此，图像形成耗时且计算昂贵，无法实现实时成像。在此，我们以三维纳米尺度X射线成像作为代表性示例，开发了一个深度学习模型来解决这一相位恢复问题。我们引入了3D-CDI-NN，这是一个深度卷积神经网络和微分编程框架，经过训练能够仅从输入的三维X射线相干散射数据中预测三维结构和应变。我们的网络被设计为在多个方面具有“物理感知”能力；即X射线散射过程的物理规律在网络训练中被明确强制执行，并且训练数据来自能够代表材料物理特性的原子级模拟。我们进一步通过基于物理的优化过程来细化神经网络预测，以在最低计算成本下实现最大精度。3D-CDI-NN能够将三维相干衍射图样反演为实空间结构和应变，速度比传统迭代相位恢复方法快数百倍，且精度损失可忽略不计。我们针对相位恢复问题的集成机器学习和微分编程解决方案广泛适用于其他应用领域的逆问题。
英文摘要：
Phase retrieval, the problem of recovering lost phase information from measured intensity alone, is an inverse problem that is widely faced in various imaging modalities ranging from astronomy to nanoscale imaging. The current process of phase recovery is iterative in nature. As a result, the image formation is time-consuming and computationally expensive, precluding real-time imaging. Here, we use 3D nanoscale X-ray imaging as a representative example to develop a deep learning model to address this phase retrieval problem. We introduce 3D-CDI-NN, a deep convolutional neural network and differential programming framework trained to predict 3D structure and strain solely from input 3D X-ray coherent scattering data. Our networks are designed to be "physics-aware" in multiple aspects; in that the physics of x-ray scattering process is explicitly enforced in the training of the network, and the training data are drawn from atomistic simulations that are representative of the physics of the material. We further refine the neural network prediction through a physics-based optimization procedure to enable maximum accuracy at lowest computational cost. 3D-CDI-NN can invert a 3D coherent diffraction pattern to real-space structure and strain hundreds of times faster than traditional iterative phase retrieval methods, with negligible loss in accuracy. Our integrated machine learning and differential programming solution to the phase retrieval problem is broadly applicable across inverse problems in other application areas.
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