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arXiv · 2402.13685

Data-Driven Forecasting of Non-Equilibrium Solid-State Dynamics

Stefan Meinecke·Felix Köster·Dominik Christiansen·Kathy Lüdge·Andreas Knorr·Malte Selig
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Abstract

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.

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Stefan Meinecke, Felix Köster, Dominik Christiansen, Kathy Lüdge, Andreas Knorr, Malte Selig. 2024-02-21. Data-Driven Forecasting of Non-Equilibrium Solid-State Dynamics. https://doi.org/10.1103/physrevb.107.184306

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