Physically implemented neural networks hold the potential to achieve the
performance of deep learning models by exploiting the innate physical
properties of devices as computational tools. This exploration of physical
processes for computation requires to also consider their intrinsic dynamics,
which can serve as valuable resources to process information. However, existing
computational methods are unable to extend the success of deep learning
techniques to parameters influencing device dynamics, which often lack a
precise mathematical description. In this work, we formulate a universal
framework to optimise interactions with dynamic physical systems in a fully
data-driven fashion. The framework adopts neural stochastic differential
equations as differentiable digital twins, effectively capturing both
deterministic and stochastic behaviours of devices. Employing differentiation
through the trained models provides the essential mathematical estimates for
optimizing a physical neural network, harnessing the intrinsic temporal
computation abilities of its physical nodes. To accurately model real devices'
behaviours, we formulated neural-SDE variants that can operate under a variety
of experimental settings. Our work demonstrates the framework's applicability
through simulations and physical implementations of interacting dynamic
devices, while highlighting the importance of accurately capturing system
stochasticity for the successful deployment of a physically defined neural
network.

通过利用设备的固有物理特性作为计算工具，我们在这项工作中制定了一个通用框架，以完全数据驱动的方式优化与动态物理系统的交互，通过模拟和实现与动态设备的相互作用进行了应用验证，并强调了准确捕捉系统随机性对于成功部署具有物理定义的神经网络的重要性。