Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach | Paper Citation Record

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This paper cites A counterexample in stochastic optimum control,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach A counterexample in stochastic optimum control,

Reference 1

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This paper cites Quadratic invariance is necessary and sufficient for convexity,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Quadratic invariance is necessary and sufficient for convexity,

Reference 2

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This paper cites Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach,

Reference 3

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This paper cites Safe learning in robotics: From learning-based control to safe reinforcement learning,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Safe learning in robotics: From learning-based control to safe reinforcement learning,

Reference 4

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This paper cites Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview,

Reference 5

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This paper cites Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methods.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methods

Reference 6

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This paper cites Learning to boost the performance of stable nonlinear systems,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Learning to boost the performance of stable nonlinear systems,

Reference 7

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This paper cites Neural Exponential Stabilization of Control-affine Nonlinear Systems.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Neural Exponential Stabilization of Control-affine Nonlinear Systems

Reference 8

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This paper cites Physics- informed machine learning for modeling and control of dynamical systems,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Physics- informed machine learning for modeling and control of dynamical systems,

Reference 9

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This paper cites Deep subspace encoders for nonlinear system identification,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Deep subspace encoders for nonlinear system identification,

Reference 10

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This paper cites Recurrent equilibrium networks: Flexible dynamic models with guaranteed stability and ro- bustness,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Recurrent equilibrium networks: Flexible dynamic models with guaranteed stability and ro- bustness,

Reference 11

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This paper cites Physically consistent neural ODEs for learning multi-physics systems,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Physically consistent neural ODEs for learning multi-physics systems,

Reference 12

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This paper cites SIMBa: System Identification Methods leveraging Backpropagation.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach SIMBa: System Identification Methods leveraging Backpropagation

Reference 13

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This paper cites Stable Linear Subspace Identification: A Machine Learning Approach.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Stable Linear Subspace Identification: A Machine Learning Approach

Reference 14

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This paper cites Neural ordinary differential equation control of dynamics on graphs,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Neural ordinary differential equation control of dynamics on graphs,

Reference 15

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This paper cites AI pontryagin or how artificial neural networks learn to control dynamical systems,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach AI pontryagin or how artificial neural networks learn to control dynamical systems,

Reference 16

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This paper cites Cautious model predic- tive control using gaussian process regression,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Cautious model predic- tive control using gaussian process regression,

Reference 17

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This paper cites Model predictive control design for dynamical systems learned by echo state networks,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Model predictive control design for dynamical systems learned by echo state networks,

Reference 18

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This paper cites On recurrent neural networks for learning-based control: recent results and ideas for future developments,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach On recurrent neural networks for learning-based control: recent results and ideas for future developments,

Reference 19

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This paper cites Learning model predictive control with long short-term memory networks,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Learning model predictive control with long short-term memory networks,

Reference 20

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This paper cites Robust classification using contractive Hamiltonian neural ODEs,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Robust classification using contractive Hamiltonian neural ODEs,

Reference 21

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This paper cites van der Schaft, L2-Gain and Passivity Techniques in Nonlinear Control, 3rd ed., ser.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach van der Schaft, L2-Gain and Passivity Techniques in Nonlinear Control, 3rd ed., ser

Reference 22

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This paper cites Communication Topology Co-Design in Graph Recurrent Neural Network Based Distributed Control.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Communication Topology Co-Design in Graph Recurrent Neural Network Based Distributed Control

Reference 23

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This paper cites Learning decentralized controllers for robot swarms with graph neural networks,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Learning decentralized controllers for robot swarms with graph neural networks,

Reference 24

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Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Graph policy gradients for large scale robot control,

Reference 25

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This paper cites Graph neural networks for distributed linear- quadratic control,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Graph neural networks for distributed linear- quadratic control,

Reference 26

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This paper cites Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning

Reference 27

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This paper cites End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks,

Reference 28

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This paper cites Safe model-based reinforcement learning with stability guarantees,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Safe model-based reinforcement learning with stability guarantees,

Reference 29

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This paper cites The Lyapunov neural network: Adaptive stability certification for safe learning of dynamical systems,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach The Lyapunov neural network: Adaptive stability certification for safe learning of dynamical systems,

Reference 30

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This paper cites Learning-based model predictive control for safe exploration,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Learning-based model predictive control for safe exploration,

Reference 31

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Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Offset- free setpoint tracking using neural network controllers,

Reference 32

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Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Learning deep energy shaping policies for stability-guaranteed manipulation,

Reference 33

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Observation cb55b4cd-f048-40f7-82d0-f4767e444688 · outbound

This paper cites Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control

Reference 34

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This paper cites Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations

Reference 35

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This paper cites Unconstrained learning of networked nonlinear systems via free parametrization of stable interconnected operators.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Unconstrained learning of networked nonlinear systems via free parametrization of stable interconnected operators

Reference 36

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This paper cites On the converse of the passivity and small-gain theorems for input–output maps,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach On the converse of the passivity and small-gain theorems for input–output maps,

Reference 37

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This paper cites A geometric integration approach to smooth optimisation: Foundations of the discrete gradient method,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach A geometric integration approach to smooth optimisation: Foundations of the discrete gradient method,

Reference 38

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Observation 1239ff71-6318-48ce-a392-175edc188535 · outbound

This paper cites Neural Distributed Controllers with Port-Hamiltonian Structures.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Neural Distributed Controllers with Port-Hamiltonian Structures

Reference 39

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Observation 6418ce43-cc65-4623-b923-8f3185a937b1 · outbound

This paper cites A Lyapunov approach to incremental stability properties,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach A Lyapunov approach to incremental stability properties,

Reference 40

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This paper cites Analysis of interconnected oscillators by dissipativity theory,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Analysis of interconnected oscillators by dissipativity theory,

Reference 41

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Observation a84ebb05-c9d7-48ea-88f7-824598f0c7a0 · outbound

This paper cites Incremental stability properties for discrete-time systems,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Incremental stability properties for discrete-time systems,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.430924Z

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Observation 9cb2476b-5558-468f-a455-9d891cad5dc8 · outbound

This paper cites On the incremental form of dissipativity,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach On the incremental form of dissipativity,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.413029Z

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Observation f8ed20ad-8953-4182-b340-642786c017da · outbound

This paper cites Arcak, C.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Arcak, C

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.397144Z

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Observation 2da90aea-130b-45e0-839a-9b8f6aa93acd · outbound

This paper cites Convex in- cremental dissipativity analysis of nonlinear systems,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Convex in- cremental dissipativity analysis of nonlinear systems,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.380987Z

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Observation 85acfb27-51e5-481e-aefb-d2ac6caac079 · outbound

This paper cites Hamil- tonian deep neural networks guaranteeing non-vanishing gradients by design,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Hamil- tonian deep neural networks guaranteeing non-vanishing gradients by design,

Reference 46

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This paper cites Universal approxi- mation property of Hamiltonian deep neural networks,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Universal approxi- mation property of Hamiltonian deep neural networks,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.364830Z

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Observation 706c3641-10c8-4a25-926c-2a178785d5ab · outbound

This paper cites Input convex neural networks,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Input convex neural networks,

Reference 48

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This paper cites Deep residual learning for image recognition,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Deep residual learning for image recognition,

Reference 49

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This paper cites Nocedal and S.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Nocedal and S

Reference 50

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Observation 4fc280f5-521a-49f6-a571-b340c1a3f20a · outbound

This paper cites Global convergence of admm in nonconvex nonsmooth optimization,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Global convergence of admm in nonconvex nonsmooth optimization,

Reference 51

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Observation 08f6ffef-4c3e-4a20-915f-2d7062e49820 · outbound

This paper cites An augmented Lagrangian based algorithm for distributed nonconvex optimization,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach An augmented Lagrangian based algorithm for distributed nonconvex optimization,

Reference 52

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Observation 7f895d4f-1f94-4b52-98c0-4d83eaa2c05a · outbound

This paper cites Large-scale nonlinear programming using ipopt: An integrating framework for enterprise-wide dynamic optimization,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Large-scale nonlinear programming using ipopt: An integrating framework for enterprise-wide dynamic optimization,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.293070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7f3b45db-83bf-43b8-9af4-7abfef503146 · outbound

This paper cites Backpropagation through time: what it does and how to do it,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Backpropagation through time: what it does and how to do it,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T20:06:27.116195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa86f9d4-639f-4f8f-8c9e-89098bdfd9e8 · outbound

This paper cites Neu- ral ordinary differential equations,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Neu- ral ordinary differential equations,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.266550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8ce9693d-d30f-403c-bb95-8e3865ab9e64 · outbound

This paper cites Foucart and H.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Foucart and H

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.250179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1238b780-2f0b-485e-a261-f3e24b3c0c29 · outbound

This paper cites Pytorch: An imperative style, high- performance deep learning library,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Pytorch: An imperative style, high- performance deep learning library,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T20:06:27.132246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba4c2a5f-7b7a-44ee-984a-1c1a1865ee39 · outbound

This paper cites Passivity indices and passivation of systems with application to systems with input/output delay,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Passivity indices and passivation of systems with application to systems with input/output delay,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.223022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2ca7a7d8-4e59-43a2-ad6a-639710b9aa3d · outbound

This paper cites Preserving and achieving passivity- short property through discretization,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Preserving and achieving passivity- short property through discretization,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.206010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e77c5a65-f3b3-4a11-ba0b-4a8eabc4995a · outbound

This paper cites Interconnection of (Q,S,R)-Dissipative Systems in Discrete Time.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Interconnection of (Q,S,R)-Dissipative Systems in Discrete Time

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T20:06:27.146451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06af3bbd-1721-4a55-997d-6adb2e0c26c5 · outbound

This paper cites Geometric nu- merical integration,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Geometric nu- merical integration,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.190257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 08385bd5-1c9c-4ab8-bb16-79186223e31b · outbound

This paper cites Time integration and discrete Hamiltonian systems,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Time integration and discrete Hamiltonian systems,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.171922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7eb50070-566a-47b3-8746-8eef7de2d449 · outbound

This paper cites On upstream differencing and godunov-type schemes for hyperbolic conservation laws,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach On upstream differencing and godunov-type schemes for hyperbolic conservation laws,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.156501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7904ec10-eef8-4edd-8a31-baf04902bcb4 · outbound

This paper cites Hamiltonian-conserving discrete canonical equa- tions based on variational difference quotients,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Hamiltonian-conserving discrete canonical equa- tions based on variational difference quotients,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.140749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0a5ea6c1-e2f0-4198-aba2-e473aabcd919 · outbound

This paper cites Control design for a class of discrete-time Port- Hamiltonian systems,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Control design for a class of discrete-time Port- Hamiltonian systems,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.124238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 695419a5-2494-4848-99c4-20d4bc1c9095 · outbound

This paper cites Robust implicit networks via non-euclidean contractions,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Robust implicit networks via non-euclidean contractions,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.107954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7ecb1cd7-9d3c-405e-92c3-4dc14a390301 · outbound

This paper cites Physics-informed implicit representations of equilibrium network flows,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Physics-informed implicit representations of equilibrium network flows,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.092441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 399ca974-fdc6-4620-bce9-c83e96f305c3 · outbound

This paper cites Ro- bustness certificates for implicit neural networks: A mixed monotone contractive approach,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Ro- bustness certificates for implicit neural networks: A mixed monotone contractive approach,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.075543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dca9f7f7-3c01-4c1d-b1f4-4425d4cd1bbf · outbound

This paper cites Monotone operator equilibrium networks,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Monotone operator equilibrium networks,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.058184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 832b37bf-4982-4122-9903-5ad39afa00be · outbound

This paper cites Stabi- lization of discrete port-Hamiltonian dynamics via interconnection and damping assignment,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Stabi- lization of discrete port-Hamiltonian dynamics via interconnection and damping assignment,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.040486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 43a1e829-3d41-4f5e-adf7-de20aa709b95 · outbound

This paper cites Distributed IDA-PBC for a class of non- holonomic mechanical systems,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Distributed IDA-PBC for a class of non- holonomic mechanical systems,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:28.018955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 513f402a-1c0d-40e2-b0bc-3005c07ce317 · outbound

This paper cites Port-Hamiltonian modelling of a differential drive mobile robot with reference velocities as inputs,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Port-Hamiltonian modelling of a differential drive mobile robot with reference velocities as inputs,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:27.990480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 18c201cb-0758-4e4f-98cc-cad76c7989dd · outbound

This paper cites Consensus-based current sharing and voltage balancing in DC microgrids with exponential loads,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Consensus-based current sharing and voltage balancing in DC microgrids with exponential loads,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:27.963375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 46e92167-444a-4983-be44-82baf70b00c2 · outbound

This paper cites Power sharing in DC microgrids,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Power sharing in DC microgrids,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:27.942122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 554a47ec-aa67-4e47-8394-4d986ec65227 · outbound

This paper cites A scal- able port-Hamiltonian approach to plug-and-play voltage stabilization in DC microgrids,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach A scal- able port-Hamiltonian approach to plug-and-play voltage stabilization in DC microgrids,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:27.920850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a2b24b00-582e-417f-994f-ae5f4c66ab9f · outbound

This paper cites Notions of input to output stability,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Notions of input to output stability,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:27.902936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 105d0ca7-269d-420e-a4f1-3944bf7a57b7 · outbound

This paper cites Review on control of DC microgrids and multiple microgrid clusters,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Review on control of DC microgrids and multiple microgrid clusters,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:27.886265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 159f2a04-9c06-4ee7-addd-c7814c56239a · outbound

This paper cites Port-Hamiltonian systems theory: An introductory overview,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Port-Hamiltonian systems theory: An introductory overview,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:27.869226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fb2a4987-7e29-42d2-a7ab-dd0aefadf2d7 · outbound

This paper cites TorchDyn: A Neural Differential Equations Library.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach TorchDyn: A Neural Differential Equations Library

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-12T20:06:27.248701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9755164-95ee-442b-ad6b-4f19a079bdb3 · outbound

This paper cites Adam: A method for stochastic gradient de- scent,.

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach Adam: A method for stochastic gradient de- scent,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:06:27.848514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Reference resolution

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