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stat.ML2024
Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling
Nicholas Galioto, Harsh Sharma, Boris Kramer +1
This paper presents a structure-preserving Bayesian approach for learning nonseparable Hamiltonian systems using stochastic dynamic models allowing for statistically-dependent, vec…
stat.ML2020★ 32 cited
Bayesian System ID: Optimal management of parameter, model, and measurement uncertainty
Nicholas Galioto, Alex Gorodetsky
We evaluate the robustness of a probabilistic formulation of system identification (ID) to sparse, noisy, and indirect data. Specifically, we compare estimators of future system be…