标题： 关于神经PDE物理解释的一篇笔记 显示英文标题

标题： A note on the physical interpretation of neural PDE's

摘要： 我们强调机器学习（ML）算法与松弛形式的离散动力系统（DDS）之间的正式和实质类比。 该类比提供了以物理信息传播过程来解释权重的透明解释，并将前向ML步骤的模型函数与相应离散动力系统的局部吸引子相匹配。 除了提高当前ML应用的可解释性外，这种类比还可能促进一类具有较少权重的新ML算法的发展。 显示英文摘要

摘要： We highlight a formal and substantial analogy between Machine Learning (ML) algorithms and discrete dynamical systems (DDS) in relaxation form. The analogy offers a transparent interpretation of the weights in terms of physical information-propagation processes and identifies the model function of the forward ML step with the local attractor of the corresponding discrete dynamics. Besides improving the explainability of current ML applications, this analogy may also facilitate the development of a new class ML algorithms with a reduced number of weights.