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
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
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
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 …
Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach
The control of large-scale cyber-physical systems requires optimal distributed policies
relying solely on limited communication with neighboring agents. However, computing …
relying solely on limited communication with neighboring agents. However, computing …
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 …
[HTML][HTML] Physics-enhanced multi-fidelity neural ordinary differential equation for forecasting long-term creep behavior of steel cables
W Zhang, SM Wang, YQ Ni, X Yuan, Y Feng… - Thin-Walled …, 2025 - Elsevier
In spatial structures, prestressed steel cables experience creep behavior when subjected to
tensile stress, resulting in stress relaxation, reduced stiffness, and potential structural failure …
tensile stress, resulting in stress relaxation, reduced stiffness, and potential structural failure …