Optimising network interactions through device agnostic models
Luca Manneschi Ian T. Vidamour Kilian D. Stenning Jack C. Gartside Charles Swindells Guru Venkat David Griffin Susan Stepney Will R. Branford Thomas Hayward Matt O Ellis Eleni Vasilaki
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.