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- W4255000698 abstract "We propose a method to discover differential equations describing the long-term dynamics of phenomena featuring a multiscale behavior in time, starting from measurements taken at the fast-scale. Our methodology is based on a synergetic combination of data assimilation, used to estimate the parameters associated with the known fast-scale dynamics, and machine learning, used to infer the laws underlying the slow-scale dynamics. By exploiting the scale separation between the fast and the slow dynamics, we propose a decoupling of time scales that allows to drastically lower the computational burden." @default.
- W4255000698 created "2022-05-12" @default.
- W4255000698 date "2021-07-01" @default.
- W4255000698 modified "2023-09-27" @default.
- W4255000698 doi "https://doi.org/10.1002/cnm.v37.7" @default.
- W4255000698 hasPublicationYear "2021" @default.
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