no code implementations • 23 Jan 2024 • Nicholas Galioto, Harsh Sharma, Boris Kramer, Alex Arkady Gorodetsky
The results show that using the Bayesian posterior as a training objective can yield upwards of 724 times improvement in Hamiltonian mean squared error using training data with up to 10% multiplicative noise compared to a standard training objective.
1 code implementation • 20 Dec 2022 • Nicholas Galioto, Alex Arkady Gorodetsky
We then analyze this objective function in the context of several state-of-the-art approaches for both linear and nonlinear system ID.
