Data-driven Surrogate Modeling for Nonlinear Material Systems in Unconventional Computing

Input-driven physical systems that exhibit nonlinear behavior are prime candidates for demonstrating unconventional computing concepts such as physical implementations of reservoir computing. Reservoir computing is a class of recurrent neural networks that utilize a simple readout layer training approach, opening the door to emulating this form of signal processing in atypical materials and physics. However, the large parameter space of these systems and the high computational expense of characterizing their performance present a challenge to efficiently matching the nonlinear dynamics with optimal computing tasks. To address this challenge, we leverage a data-driven, physics-agnostic modeling technique based on Koopman theory to produce a low-cost surrogate of the reservoir dynamics for benchmarking. This approach combines an underlying linear time invariant system with a nonlinear mapping. Preliminary results indicate a 150x speed-up in time series data generation of the network dynamics compared to Runge-Kutta iteration, which is critical for efficient benchmarking of reservoir performance (since benchmarking requires repeated simulation). Moreover, as we will demonstrate, the model parameters can be directly interpreted to yield insight into the behavior of the system of interest.