Learning Dissipative Dynamics with Dissipativity-by-Construction Discrete-Time Neural Networks

Dissipativity is a fundamental system-theoretic property closely related to stability, passivity, and input--output stability, and is particularly important in robotics, where learned dynamics models are often embedded within feedback control loops. However, most existing approaches for learning dis