A co-learning approach jointly optimizes a port-Hamiltonian system model and an energy-balancing passivity-based controller from data via alternating optimization with neural networks that embed structure for guaranteed passivity and stability.
Stable port-hamiltonian neural networks
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
A framework builds stable neural models of turbulent dynamics by enforcing energy-preserving nonlinearities and causal constraints in discrete-time flow maps, demonstrated on Charney-DeVore and Lorenz-96 systems.
A learned context-energy term in port-Hamiltonian policies creates selective risk navigation that activates evasive forces only when safer paths are available.
citing papers explorer
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Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control
A co-learning approach jointly optimizes a port-Hamiltonian system model and an energy-balancing passivity-based controller from data via alternating optimization with neural networks that embed structure for guaranteed passivity and stability.
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Physics and causally constrained discrete-time neural models of turbulent dynamical systems
A framework builds stable neural models of turbulent dynamics by enforcing energy-preserving nonlinearities and causal constraints in discrete-time flow maps, demonstrated on Charney-DeVore and Lorenz-96 systems.
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Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation
A learned context-energy term in port-Hamiltonian policies creates selective risk navigation that activates evasive forces only when safer paths are available.