Adaptive Locomotion of Soft Microrobots
Networked Control and Communication
Controller Learning using Bayesian Optimization
Event-based Wireless Control of Cyber-physical Systems
Model-based Reinforcement Learning for PID Control
Learning Probabilistic Dynamics Models
Gaussian Filtering as Variational Inference
Machine Learning for Understanding Quantum Systems
We are interested in deploying machine learning methods to improve the understanding of quantum systems. Given a physical system in which a subsystem is embedded in a larger system, i.e. a bath, we used neural networks to learn the generator of the subsystem's dynamics. The network was trained with pre-simulated trajectories of Bloch vectors arising from different initial states.
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