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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.14913931 |
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| _version_ | 1866902108952854528 |
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| author | Gao, Hang Kaltenbach, Sebastian Koumoutsakos, Petros |
| author_facet | Gao, Hang Kaltenbach, Sebastian Koumoutsakos, Petros |
| contents | <p>We introduce generative models for accelerating simulations of complex systems through learning and evolving their effective dynamics. In the proposed Generative Learning of Effective Dynamics (G-LED), instancesof high dimensional data are down sampled to a lower dimensional manifold that is evolved through an autoregressive attention mechanism. In turn, Bayesian diffusion models, that map this low-dimensional manifold onto its corresponding high-dimensional space, capture the statistics of the system dynamics. We demonstrate the capabilities and drawbacks of G-LED in simulations of several benchmark systems, including the Kuramoto–Sivashinsky (KS) equation, two-dimensional high Reynolds number flow over a backward-facing step, and simulations of three-dimensional turbulent channel flow. The results demonstrate that generative learning offers new frontiers for the accurate forecasting of the statistical properties of complex systems at a reduced computational cost.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14913931 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Generative Learning for Forecasting the Dynamics of Complex Systems Gao, Hang Kaltenbach, Sebastian Koumoutsakos, Petros <p>We introduce generative models for accelerating simulations of complex systems through learning and evolving their effective dynamics. In the proposed Generative Learning of Effective Dynamics (G-LED), instancesof high dimensional data are down sampled to a lower dimensional manifold that is evolved through an autoregressive attention mechanism. In turn, Bayesian diffusion models, that map this low-dimensional manifold onto its corresponding high-dimensional space, capture the statistics of the system dynamics. We demonstrate the capabilities and drawbacks of G-LED in simulations of several benchmark systems, including the Kuramoto–Sivashinsky (KS) equation, two-dimensional high Reynolds number flow over a backward-facing step, and simulations of three-dimensional turbulent channel flow. The results demonstrate that generative learning offers new frontiers for the accurate forecasting of the statistical properties of complex systems at a reduced computational cost.</p> |
| title | Generative Learning for Forecasting the Dynamics of Complex Systems |
| url | https://doi.org/10.5281/zenodo.14913931 |