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We introduce a comprehensive data-driven framework aimed at enhancing the modeling of physical systems, employing inference techniques and machine learning enhancements.
arXiv:https://doi.org/10.1080/14786440109462590
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arXiv:https://pubs.aip.org/aip/pof/article-pdf/doi/10.1063/1.5113494/14800114/085101\_1\_online.pdf
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arXiv:https://pubs.aip.org/aip/pof/article-pdf/doi/10.1063/5.0061577/13869240/091301\_1\_online.pdf
S. E. Ahmed, S. Pawar, O. San, A. Rasheed, T. Iliescu, B. R. Noack, On closures for reduced order models—A spectrum of first-principle to machine-learned avenues, Physics of Fluids 33 (9) (2021) 091301 · 2021
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R. T. Q. Chen, B. Amos, M. Nickel, Learning neural event functions for ordinary differential equations, in: International Conference on Learning Representations, 2021
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