5 papers
Reverse Flow Matching: A Unified Framework for Online Reinforcement Learning with Diffusion and Flow Policies
Zeyang Li, Sunbochen Tang, Navid Azizan
Diffusion and flow policies are gaining prominence in online reinforcement learning (RL) due to their expressive power, yet training them efficiently remains a critical challenge.…
LMI-Net: Linear Matrix Inequality--Constrained Neural Networks via Differentiable Projection Layers
Sunbochen Tang, Andrea Goertzen, Navid Azizan
Linear matrix inequalities (LMIs) have played a central role in certifying stability, robustness, and forward invariance of dynamical systems. Despite rapid development in learning…
Learning Dissipative Chaotic Dynamics with Boundedness Guarantees
Sunbochen Tang, Themistoklis Sapsis, Navid Azizan
Chaotic dynamics, commonly seen in weather systems and fluid turbulence, are characterized by their sensitivity to initial conditions, which makes accurate prediction challenging.…
ECO: Energy-Constrained Operator Learning for Chaotic Dynamics with Boundedness Guarantees
Andrea Goertzen, Sunbochen Tang, Navid Azizan
Chaos is a fundamental feature of many complex dynamical systems, including weather systems and fluid turbulence. These systems are inherently difficult to predict due to their ext…
Meta-Learning for Adaptive Control with Automated Mirror Descent
Sunbochen Tang, Haoyuan Sun, Navid Azizan
Adaptive control achieves concurrent parameter learning and stable control under uncertainties that are linearly parameterized with known nonlinear features. Nonetheless, it is oft…