Search Results for author: Jacob Seidman

Found 2 papers, 1 papers with code

Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior

no code implementations15 May 2023 Thomas Beckers, Jacob Seidman, Paris Perdikaris, George J. Pappas

Data-driven approaches achieve remarkable results for the modeling of complex dynamics based on collected data.

Uncertainty Quantification

Learning Operators with Coupled Attention

1 code implementation4 Jan 2022 Georgios Kissas, Jacob Seidman, Leonardo Ferreira Guilhoto, Victor M. Preciado, George J. Pappas, Paris Perdikaris

Supervised operator learning is an emerging machine learning paradigm with applications to modeling the evolution of spatio-temporal dynamical systems and approximating general black-box relationships between functional data.

Operator learning

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