Thermodynamics could be seen as an expression of physics at a high epistemic level. As
such, its potential as an inductive bias to help machine learning procedures attain accurate …

The field of automated lung cancer diagnosis using Computed Tomography (CT) scans has
been significantly advanced by the precise predictions offered by Convolutional Neural …

Large-scale cyber-physical systems require that control policies are distributed, that is, that
they only rely on local real-time measurements and communication with neighboring agents …

Deep neural networks can be fragile and sensitive to small input perturbations that might
cause a significant change in the output. In this letter, we employ contraction theory to …

Despite the immense success of neural networks in modeling system dynamics from data,
they often remain physics-agnostic black boxes. In the particular case of physical systems …

In this work, we introduce and study a class of Deep Neural Networks (DNNs) in continuous-
time. The proposed architecture stems from the combination of Neural Ordinary Differential …

Accurate models of robot dynamics are critical for safe and stable control and generalization
to novel operational conditions. Hand-designed models, however, may be insufficiently …

This paper presents LEMURS, an algorithm for learning scalable multi-robot control policies
from cooperative task demonstrations. We propose a port-Hamiltonian description of the …

Neural networks have gained much interest because of their effectiveness in many
applications. However, their mathematical properties are generally not well understood. If …

This letter investigates the universal approximation capabilities of Hamiltonian Deep Neural
Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary …