Data-driven forecasting of nonequilibrium solid-state dynamics

We present a data-driven approach to efficiently approximate nonlinear transient dynamics in solid-state systems. Our proposed machine-learning model combines a dimensionality reduction stage with a nonlinear vector autoregression scheme. We report an outstanding time-series forecasting performance combined with an easy-to-deploy model and an inexpensive training routine. Our results are of great relevance as they have the potential to massively accelerate multiphysics simulation software and thereby guide the future development of solid-state-based technologies.

Automated summary

We propose a data-driven approach to approximate nonlinear transient dynamics in solid-state systems efficiently. Our machine-learning model combines dimensionality reduction with a nonlinear vector autoregression scheme. We present outstanding time-series forecasting performance, along with an easy-to-deploy model and an inexpensive training routine.