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- W4314946873 abstract "Data-driven approaches achieve remarkable results for the modeling of complex dynamics based on collected data. However, these models often neglect basic physical principles which determine the behavior of any real-world system. This omission is unfavorable in two ways: The models are not as data-efficient as they could be by incorporating physical prior knowledge, and the model itself might not be physically correct. We propose Gaussian Process Port-Hamiltonian systems (GP-PHS) as a physics-informed Bayesian learning approach with uncertainty quantification. The Bayesian nature of GP-PHS uses collected data to form a distribution over all possible Hamiltonians instead of a single point estimate. Due to the underlying physics model, a GP-PHS generates passive systems with respect to designated inputs and outputs. Further, the proposed approach preserves the compositional nature of Port-Hamiltonian systems." @default.
- W4314946873 created "2023-01-10" @default.
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- W4314946873 date "2022-12-06" @default.
- W4314946873 modified "2023-09-29" @default.
- W4314946873 title "Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior" @default.
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- W4314946873 doi "https://doi.org/10.1109/cdc51059.2022.9992733" @default.
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