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Found 6 papers, 2 papers with code
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification
Switching physical systems are ubiquitous in modern control applications, for instance, locomotion behavior of robots and animals, power converters with switches and diodes.
Leveraging Predictive Models for Adaptive Sampling of Spatiotemporal Fluid Processes
Our algorithm makes use of the predictions from a learned prediction model to plan a path for an autonomous vehicle to adaptively and efficiently survey the region of interest.
Online Dynamics Learning for Predictive Control with an Application to Aerial Robots
To improve the adaptiveness of the model and the controller, we propose an online dynamics learning framework that continually improves the accuracy of the dynamic model during deployment.
KNODE-MPC: A Knowledge-based Data-driven Predictive Control Framework for Aerial Robots
Using a quadrotor, we benchmark our hybrid model against a state-of-the-art Gaussian Process (GP) model and show that the hybrid model provides more accurate predictions of the quadrotor dynamics and is able to generalize beyond the training data.
NODEO: A Neural Ordinary Differential Equation Based Optimization Framework for Deformable Image Registration
Deformable image registration (DIR), aiming to find spatial correspondence between images, is one of the most critical problems in the domain of medical image analysis.
Knowledge-Based Learning of Nonlinear Dynamics and Chaos
For the Lorenz system, different types of domain knowledge are incorporated to demonstrate the strength of knowledge embedding in data-driven system identification.
