Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks | Paper Citation Record

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This paper cites Data-driven Bayesian Control of Port-Hamiltonian Systems.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Data-driven Bayesian Control of Port-Hamiltonian Systems

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This paper cites Version 0.3.13.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Version 0.3.13

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This paper cites Neural ordinary differential equations.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Neural ordinary differential equations

Reference 3

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This paper cites A robust consensus algorithm for current sharing and voltage regulation in DC microgrids.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks A robust consensus algorithm for current sharing and voltage regulation in DC microgrids

Reference 4

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This paper cites Port-Hamiltonian neural networks for learning explicit time-dependent dynamical systems.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Port-Hamiltonian neural networks for learning explicit time-dependent dynamical systems

Reference 5

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This paper cites How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations

Reference 6

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This paper cites Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling

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This paper cites Springer Science & Busi- ness Media, 2009.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Springer Science & Busi- ness Media, 2009

Reference 8

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This paper cites Port-Hamiltonian Neural ODE Networks on Lie Groups for Robot Dynamics Learning and Control.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Port-Hamiltonian Neural ODE Networks on Lie Groups for Robot Dynamics Learning and Control

Reference 9

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This paper cites Impulses and Physiological States in Theoretical Models of Nerve Membrane.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Impulses and Physiological States in Theoretical Models of Nerve Membrane

Reference 10

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

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Distributed neural network control with depend- ability guarantees: a compositional port-Hamiltonian approach

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This paper cites Hamilto- nian Neural Networks.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Hamilto- nian Neural Networks

Reference 12

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This paper cites Dynamic iteration schemes and port- Hamiltonian formulation in coupled differential-algebraic equation circuit simulation.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Dynamic iteration schemes and port- Hamiltonian formulation in coupled differential-algebraic equation circuit simulation

Reference 13

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This paper cites Solving high- dimensional partial differential equations using deep learning.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Solving high- dimensional partial differential equations using deep learning

Reference 14

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This paper cites Version 0.0.13.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Version 0.0.13

Reference 15

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This paper cites MINN: Learning the dynamics of differential- algebraic equations and application to battery modeling.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks MINN: Learning the dynamics of differential- algebraic equations and application to battery modeling

Reference 16

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This paper cites FitzHugh-Nagumo model.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks FitzHugh-Nagumo model

Reference 17

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This paper cites Physics-informed machine learning.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Physics-informed machine learning

Reference 18

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This paper cites On neural differential equations.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks On neural differential equations

Reference 19

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This paper cites Learning Neural Differential Algebraic Equations via Operator Splitting.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Learning Neural Differential Algebraic Equations via Operator Splitting

Reference 20

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This paper cites PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network

Reference 21

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This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 22

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This paper cites Physics-informed neural networks with hard constraints for inverse design.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Physics-informed neural networks with hard constraints for inverse design

Reference 23

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This paper cites Structure-preserving discretization for port-Hamiltonian descriptor systems.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Structure-preserving discretization for port-Hamiltonian descriptor systems

Reference 24

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This paper cites DAE-PINN: a physics-informed neural network model for simulating differential algebraic equations with application to power networks.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks DAE-PINN: a physics-informed neural network model for simulating differential algebraic equations with application to power networks

Reference 25

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This paper cites An Active Pulse Transmission Line Simulating Nerve Axon.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks An Active Pulse Transmission Line Simulating Nerve Axon

Reference 26

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This paper cites Engineering AI systems and AI for engineering: compositionality and physics in learning.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Engineering AI systems and AI for engineering: compositionality and physics in learning

Reference 27

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This paper cites Compositional learning of dynamical system models using port-hamiltonian neural networks.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Compositional learning of dynamical system models using port-hamiltonian neural networks

Reference 28

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This paper cites Total Energy Shaping with Neural Interconnection and Damping Assignment - Passivity Based Control.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Total Energy Shaping with Neural Interconnection and Damping Assignment - Passivity Based Control

Reference 29

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Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Universal Differential Equations for Scientific Machine Learning

Reference 30

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This paper cites Deep hidden physics models: Deep learning of nonlinear partial differential equations.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Deep hidden physics models: Deep learning of nonlinear partial differential equations

Reference 31

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This paper cites Physics- informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differen- tial equations.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Physics- informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differen- tial equations

Reference 32

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This paper cites DGM: A deep learning algorithm for solving partial differential equations.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks DGM: A deep learning algorithm for solving partial differential equations

Reference 33

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This paper cites Physics-constrained learning of PDE systems with uncertainty quantified port- Hamiltonian models.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Physics-constrained learning of PDE systems with uncertainty quantified port- Hamiltonian models

Reference 34

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This paper cites Trefethen and David Bau III.Numerical Linear Algebra.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Trefethen and David Bau III.Numerical Linear Algebra

Reference 35

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

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Port-Hamiltonian systems theory: An introductory overview

Reference 36

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This paper cites Port-Hamiltonian systems on graphs.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Port-Hamiltonian systems on graphs

Reference 37

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Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Unresolved cited work

Reference 38

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This paper cites Feasibility study of neural ode and dae modules for power system dynamic component modeling.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Feasibility study of neural ode and dae modules for power system dynamic component modeling

Reference 39

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This paper cites Neural Energy Casimir Control for Port-Hamiltonian Systems.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Neural Energy Casimir Control for Port-Hamiltonian Systems

Reference 40

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This paper cites Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data

Reference 41

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This paper cites For example, renewable energy sources can act as the power generation unit in the DGU model.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks For example, renewable energy sources can act as the power generation unit in the DGU model

Reference 42

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Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks Unresolved cited work

Reference 43

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This paper cites In the compositional learning experiments, the microgrid has a complete graph configuration with 10 nodes, with DGUs at the nodes and transmission lines at the edges.

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks In the compositional learning experiments, the microgrid has a complete graph configuration with 10 nodes, with DGUs at the nodes and transmission lines at the edges

Reference 44

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