Data-driven forecasting of nonequilibrium solid-state dynamics

被引：3

作者：

Meinecke, Stefan ^{[1
]}

Koester, Felix ^{[1
]}

Christiansen, Dominik ^{[1
]}

Luedge, Kathy ^{[2
]}

Knorr, Andreas ^{[1
]}

Selig, Malte ^{[1
]}

机构：

[1] Tech Univ Berlin, Inst Theoret Phys, Hardenbergstr 36, D-10623 Berlin, Germany

[2] Tech Univ Ilmenau, Inst Phys, Weimarer Str 25, D-98693 Ilmenau, Germany

来源：

PHYSICAL REVIEW B | 2023年 / 107卷 / 18期

关键词：

NEURAL-NETWORK; MACHINE; APPROXIMATION; GENERATION; COMPLEX; WIENER; MEMORY; MODEL;

D O I：

10.1103/PhysRevB.107.184306

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页数：18

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