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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.13685 |
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| _version_ | 1866914687824691200 |
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| author | Meinecke, Stefan Köster, Felix Christiansen, Dominik Lüdge, Kathy Knorr, Andreas Selig, Malte |
| author_facet | Meinecke, Stefan Köster, Felix Christiansen, Dominik Lüdge, Kathy Knorr, Andreas Selig, Malte |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13685 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-Driven Forecasting of Non-Equilibrium Solid-State Dynamics Meinecke, Stefan Köster, Felix Christiansen, Dominik Lüdge, Kathy Knorr, Andreas Selig, Malte Computational Physics 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. |
| title | Data-Driven Forecasting of Non-Equilibrium Solid-State Dynamics |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2402.13685 |