4 citations · 7 across the 14 of their papers we have counts for
3 papers · 1 filter
Universal Approximation Property of Hamiltonian Deep Neural Networks
Muhammad Zakwan, Massimiliano d'Angelo, Giancarlo Ferrari-Trecate
This paper investigates the universal approximation capabilities of Hamiltonian Deep Neural Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary Diffe…
Physically Consistent Neural ODEs for Learning Multi-Physics Systems
Muhammad Zakwan, Loris Di Natale, Bratislav Svetozarevic +3
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, th…
Robust Classification using Contractive Hamiltonian Neural ODEs
Muhammad Zakwan, Liang Xu, Giancarlo Ferrari-Trecate
Deep neural networks can be fragile and sensitive to small input perturbations that might cause a significant change in the output. In this paper, we employ contraction theory to i…