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Learning Constrained Dynamical Embeddings for Geophysical Dynamics

Abstract : In this work, we investigate the implementation of physical constraints for the regularization of linear quadratic dynamical representations of partially observed systems. We focus on energy preserving quadratic terms and propose to enforce this constraint within the learning criterion of the models. We further demonstrate on the Lorenz 63 system that the generalization performance is significantly improved to states beyond the attractor spanned by the observation data when this constraint is satisfied.
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Contributor : Ronan Fablet <>
Submitted on : Wednesday, November 18, 2020 - 9:57:18 AM
Last modification on : Thursday, December 3, 2020 - 8:48:45 AM


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  • HAL Id : hal-02285700, version 1


Said Ouala, Steven L Brunton, Duong Nguyen, Lucas Drumetz, Ronan Fablet. Learning Constrained Dynamical Embeddings for Geophysical Dynamics. CI 2019 : 9th International Workshop on Climate Informatics, 2019, Paris, France. ⟨hal-02285700⟩



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