42 citations · 42 across the 2 of their papers we have counts for
3 papers
Stochastic Port-Hamiltonian Gaussian Processes for Learning from Noisy Dynamics on Lie Groups
Thribhuvan Rapolu, Luke McLennan, Chandrajit Bajaj
Learning predictive models for dynamics is difficult because observations are often sparse and noisy, while the underlying dynamics evolve on nonlinear configuration manifolds and…
Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data
Luke McLennan, Yi Wang, Ryan Farell +2
We introduce a robust framework for learning various generalized Hamiltonian dynamics from noisy, sparse phase-space data and in an unsupervised manner based on variational Bayesia…
Recipes for when Physics Fails: Recovering Robust Learning of Physics Informed Neural Networks
Chandrajit Bajaj, Luke McLennan, Timothy Andeen +1
Physics-informed Neural Networks (PINNs) have been shown to be effective in solving partial differential equations by capturing the physics induced constraints as a part of the tra…