  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 multi-physics simulation software and thereby guide to
future development of solid-state based technologies.
