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Structure-Aware Spatial Kernels for Gaussian Process Distributed Port-Hamiltonian Systems
Tianyi Yang, Thomas Beckers
Gaussian process distributed port-Hamiltonian systems (GP-dPHS) enable data-driven modeling of distributed dynamical systems while preserving their physical structure. However, sta…
Identify then Realize: Contrastive Learning of Latent Port-Hamiltonian Dynamics from Partial Observations
Peilun Li, Kaiyuan Tan, Daniel Moyer +1
Identifying latent state representations and dynamics is essential when direct modeling in observation space is infeasible, particularly under partial and high-dimensional observat…
PHDME: Physics-Informed Diffusion Models without Explicit Governing Equations
Kaiyuan Tan, Kendra Givens, Peilun Li +1
Diffusion models provide expressive priors for forecasting trajectories of dynamical systems, but are typically unreliable in the sparse data regime. Physics-informed machine learn…
Physics-Constrained Learning for PDE Systems with Uncertainty Quantified Port-Hamiltonian Models
Kaiyuan Tan, Peilun Li, Thomas Beckers
Modeling the dynamics of flexible objects has become an emerging topic in the community as these objects become more present in many applications, e.g., soft robotics. Due to the p…