Data-Driven Forecasting of Non-Equilibrium Solid-State Dynamics
arXiv:2402.13685 · doi:10.1103/PhysRevB.107.184306
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.
The simulation code and the regression code is available on GitHub under MIT license (https://github.com/stmeinecke/derrom)
References in corpus (26)
- Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
- Machine Learning for Fluid Mechanics
- Facial Expression Analysis under Partial Occlusion: A Survey
- k.p theory for two-dimensional transition metal dichalcogenide semiconductors
- Deep Face Recognition: A Survey
- Next Generation Reservoir Computing
- Intrinsic Electrical Transport Properties of Monolayer Silicene and MoS2 from First Principles
- Intrinsic Transport Properties of Electrons and Holes in Monolayer Transition Metal Dichalcogenides
- Machine Learning Energies of 2 M Elpasolite (ABCD) Crystals
- Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks
- Representation of compounds for machine-learning prediction of physical properties
- Predicting the thermodynamic stability of perovskite oxides using machine learning models
- Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows
- Quantization of quasinormal modes for open cavities and plasmonic cavity-QED
- On Explaining the Surprising Success of Reservoir Computing Forecaster of Chaos? The Universal Machine Learning Dynamical System with Contrasts to VAR and DMD
- Experimental neural network enhanced quantum tomography
- Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM
- Designing nanostructures for interfacial phonon transport via Bayesian optimization
- Machine Learning on Neutron and X-Ray Scattering
- Exciton propagation and halo formation in two-dimensional materials
- Ultrafast dynamics in monolayer TMDCs: the interplay of dark excitons, phonons and intervalley Coulomb exchange
- Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computing
- Hybrid quantum-classical modeling of quantum dot devices
- Strong coupling regime and hybrid quasinormal modes of a single plasmonic resonator coupled to a TMDC monolayer
- Feedback induced locking in semiconductor lasers with strong amplitude-phase coupling
- Sampling asymmetric open quantum systems for artificial neural networks