1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.RO2025
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…
cs.LG2024★ 1 cited
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…