1 citations · 1 across the 3 of their papers we have counts for
5 papers
Multiple fidelities and joint numerical range
Pei Li, Bang-Hai Wang
We investigate the effectiveness of entanglement detection based on multiple fidelities via the geometry of the joint separable numerical range. When all reference states are produ…
Identify Then Project: Contrastive Learning of Latent Dynamics from Partial Observations with Port-Hamiltonian Structure
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…
Plug-and-Play Physics-informed Learning using Uncertainty Quantified Port-Hamiltonian Models
Kaiyuan Tan, Peilun Li, Jun Wang +1
The ability to predict trajectories of surrounding agents and obstacles is a crucial component in many robotic applications. Data-driven approaches are commonly adopted for state p…
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…