Learning robust state observers using neural odes
Relying on recent research results on Neural ODEs, this paper presents a methodology for
the design of state observers for nonlinear systems based on Neural ODEs, learning …
the design of state observers for nonlinear systems based on Neural ODEs, learning …
On robustness of neural ODEs image classifiers
Abstract Neural Ordinary Differential Equations (Neural ODEs), as a family of novel deep
models, delicately link conventional neural networks and dynamical systems, which bridges …
models, delicately link conventional neural networks and dynamical systems, which bridges …
Physically consistent neural ODEs for learning multi-physics systems
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 …
they often remain physics-agnostic black boxes. In the particular case of physical systems …
Unconstrained parametrization of dissipative and contracting neural ordinary differential equations
D Martinelli, CL Galimberti… - 2023 62nd IEEE …, 2023 - ieeexplore.ieee.org
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 …
time. The proposed architecture stems from the combination of Neural Ordinary Differential …
Dynamical systems–based neural networks
Neural networks have gained much interest because of their effectiveness in many
applications. However, their mathematical properties are generally not well understood. If …
applications. However, their mathematical properties are generally not well understood. If …
Universal approximation property of Hamiltonian deep neural networks
M Zakwan, M d'Angelo… - IEEE Control Systems …, 2023 - ieeexplore.ieee.org
This letter investigates the universal approximation capabilities of Hamiltonian Deep Neural
Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary …
Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary …
Neural Distributed Controllers with Port-Hamiltonian Structures
M Zakwan, G Ferrari-Trecate - arXiv preprint arXiv:2403.17785, 2024 - arxiv.org
Controlling large-scale cyber-physical systems necessitates optimal distributed policies,
relying solely on local real-time data and limited communication with neighboring agents …
relying solely on local real-time data and limited communication with neighboring agents …
Certifiably robust neural ode with learning-based barrier function
Neural Ordinary Differential Equations (ODEs) have gained traction in many applications.
While recent studies have focused on empirically increasing the robustness of neural ODEs …
While recent studies have focused on empirically increasing the robustness of neural ODEs …
Efficient Certified Training and Robustness Verification of Neural ODEs
Neural Ordinary Differential Equations (NODEs) are a novel neural architecture, built around
initial value problems with learned dynamics which are solved during inference. Thought to …
initial value problems with learned dynamics which are solved during inference. Thought to …
[PDF][PDF] Neural-rendezvous: Learning-based robust guidance and control to encounter interstellar objects
H Tsukamoto, SJ Chung, B Donitz… - arXiv preprint …, 2022 - authors.library.caltech.edu
I objects (ISOs) represent one of the last unexplored classes of solar system objects. They
are active or inert objects passing through our solar system on an unbound hyperbolic …
are active or inert objects passing through our solar system on an unbound hyperbolic …