Cover Image

Hopfield neural networks as port-Hamiltonian and gradient systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	van der Schaft, Arjan
Format:	Preprint
Published:	2026
Subjects:	Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Port-Hamiltonian nonlinear systems
by: van der Schaft, Arjan
Published: (2024)

Reciprocity of nonlinear systems
by: van der Schaft, Arjan
Published: (2023)

Port-Hamiltonian systems with energy and power ports
by: Krhac, Kaja, et al.
Published: (2024)

Maximum Power Transfer for Nonlinear State Space Systems
by: van der Schaft, Arjan
Published: (2025)

Singular networks and ultrasensitive terminal behaviors
by: Franci, Alessio, et al.
Published: (2025)

Symmetry in linear physical systems
by: van der Schaft, Arjan, et al.
Published: (2025)

Kron reduction of nonlinear networks
by: van der Schaft, Arjan, et al.
Published: (2024)

Observer design for classes of nonlinear port-Hamiltonian systems
by: Ugolini, Filippo, et al.
Published: (2026)

Energy-optimal control of discrete-time port-Hamiltonian systems
by: Sarkar, Arijit, et al.
Published: (2025)

On Port-Hamiltonian Formulation of Hysteretic Energy Storage Elements: The Backlash Case
by: Keulen, Jurrien, et al.
Published: (2026)

Finding the nearest bounded-real port-Hamiltonian system
by: Cherifi, Karim, et al.
Published: (2025)

The distance-based formation controller design for multi-agent systems in port-Hamiltonian form
by: Zhao, Jingyi, et al.
Published: (2026)

Infinite-dimensional port-Hamiltonian systems with a stationary interface
by: Kilian, Alexander, et al.
Published: (2023)

Convergence of gradient flow for learning convolutional neural networks
by: Diederen, Jona-Maria, et al.
Published: (2026)

Solvability of time-varying infinite-dimensional linear port-Hamiltonian systems
by: Kurula, Mikael
Published: (2022)

Observer-Based State Feedback Controller for a Mindlin Plate Model in port-Hamiltonian framework
by: Alastuey, Ignacio Diaz, et al.
Published: (2026)

The duality structure gradient descent algorithm: analysis and applications to neural networks
by: Flynn, Thomas
Published: (2017)

Two energy methods for distributed port-Hamiltonian systems and their application to stability analysis
by: Roschkowski, Marco, et al.
Published: (2025)

Convergence of continuous-time stochastic gradient descent with applications to deep neural networks
by: Lugosi, Gabor, et al.
Published: (2024)

Segmented Model-Based Hydrogen Delivery Control for PEM Fuel Cells: a Port-Hamiltonian Approach
by: Kumar, Lalitesh, et al.
Published: (2024)

Non-asymptotic convergence analysis of the stochastic gradient Hamiltonian Monte Carlo algorithm with discontinuous stochastic gradient with applications to training of ReLU neural networks
by: Liang, Luxu, et al.
Published: (2024)

Recurrent neural networks: vanishing and exploding gradients are not the end of the story
by: Zucchet, Nicolas, et al.
Published: (2024)

Nonsmooth Optimisation and neural networks
by: Peiris, Vinesha, et al.
Published: (2025)

Convergence of stochastic gradient descent under a local Lojasiewicz condition for deep neural networks
by: An, Jing, et al.
Published: (2023)

Optimal control of port-Hamiltonian systems: energy, entropy, and exergy
by: Philipp, Friedrich, et al.
Published: (2023)

Generic controllability of equivariant systems and applications to particle systems and neural networks
by: Agrachev, Andrei, et al.
Published: (2024)

A neural network-based approach to hybrid systems identification for control
by: Fabiani, Filippo, et al.
Published: (2024)

A mixed-integer framework for analyzing neural network-based controllers for piecewise affine systems with bounded disturbances
by: Teichrib, Dieter, et al.
Published: (2025)

Port-Hamiltonian structures in infinite-dimensional optimal control: Primal-Dual gradient method and control-by-interconnection
by: Gernandt, Hannes, et al.
Published: (2024)

Efficient model predictive control for nonlinear systems modelled by deep neural networks
by: Lan, Jianglin
Published: (2024)

Robust design optimization for a nonlinear system via Bayesian neural network enhanced polynomial dimensional decomposition
by: Jang, Hyunho, et al.
Published: (2026)

Closed-loop training of static output feedback neural network controllers for large systems: A distillation case study
by: Turan, E. M., et al.
Published: (2024)

Contracting Strategies for Electrolyzers to Secure Grid Connection: The Dutch Case
by: Swarts, Thomas, et al.
Published: (2025)

KKT-based optimality conditions for neural network approximation
by: Peiris, Vinesha, et al.
Published: (2025)

Robust stabilization of polytopic systems via fast and reliable neural network-based approximations
by: Fabiani, Filippo, et al.
Published: (2022)

Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks
by: Jentzen, Arnulf, et al.
Published: (2024)

Adversarial neural network methods for topology optimization of eigenvalue problems
by: Hu, Xindi, et al.
Published: (2024)

Error bounds for maxout neural network approximations of model predictive control
by: Teichrib, Dieter, et al.
Published: (2023)

On second order conditions for singular optimal control of port-Hamiltonian systems
by: Aronna, M. Soledad, et al.
Published: (2024)

PG-Flow: Deterministic implicit policy gradients for geometric product-form queueing networks
by: Mahjoub, Youssef Ait El
Published: (2025)