论文标题
如何进行基于物理学的学习
How to do Physics-based Learning
论文作者
论文摘要
本教程的目的是解释如何实施基于物理学的学习来快速原型计算成像系统。我们提供了基于物理学的学习，基于物理的网络的构建以及其减少实践的基本概述。具体来说，我们提倡两次利用自动分化功能，一次建立基于物理的网络，然后再次进行基于物理学的学习。因此，用户只需要为其系统实施远期模型流程，从而加快了原型制作时间。我们提供基于物理的网络和培训程序的开源Pytorch实现，以解决通用稀疏恢复问题
The goal of this tutorial is to explain step-by-step how to implement physics-based learning for the rapid prototyping of a computational imaging system. We provide a basic overview of physics-based learning, the construction of a physics-based network, and its reduction to practice. Specifically, we advocate exploiting the auto-differentiation functionality twice, once to build a physics-based network and again to perform physics-based learning. Thus, the user need only implement the forward model process for their system, speeding up prototyping time. We provide an open-source Pytorch implementation of a physics-based network and training procedure for a generic sparse recovery problem
