Many dynamical systems—from robots interacting with their surroundings to large-scale
multi-physics systems—involve a number of interacting subsystems. Toward the objective of …

This paper presents a framework for physics-informed energy-based neural network (NN)
design to learn models of interconnected systems under the port-Hamiltonian (pH) …

In this work, we show the potential of the universal approximation property of neural
networks in the design of interconnection and damping assignment passivity-based …

Deep neural networks (DNN) are increasingly being used to learn controllers due to their
excellent approximation capabilities. However, their black-box nature poses significant …

This paper proposes a novel learning-based approach for achieving exponential
stabilization of nonlinear control-affine systems. We leverage the Control Contraction Metrics …