Hamiltonian Neural Networks
PulseAugur coverage of Hamiltonian Neural Networks — every cluster mentioning Hamiltonian Neural Networks across labs, papers, and developer communities, ranked by signal.
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Hamiltonian Neural Networks show improved long-horizon prediction accuracy
Researchers have evaluated Hamiltonian Neural Networks (HNNs) against standard feedforward networks for predicting pendulum and Kepler dynamics. By using identical training data, optimization settings, and integration s…
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New neural network predicts Hamiltonian chaos beyond training data
Researchers have developed a new type of Hamiltonian neural network (RF-HNN) capable of predicting Hamiltonian chaos in dynamical systems, even in regimes not present in its training data. Unlike conventional HNNs, the …
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Hamiltonian Neural Networks explained via differential geometry
A company blog post explores Hamiltonian Neural Networks (HNNs) through the lens of differential geometry, offering an alternative perspective to the typical loss-function-focused explanations. The author highlights the…
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Symplectic Neural Networks enhance Hamiltonian Neural Network training
Researchers have developed Symplectic Neural Networks (SNNs) to improve the training of Hamiltonian Neural Networks (HNNs). This new method addresses the computational challenges associated with implicit symplectic inte…
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NEXUS framework models physically consistent 3D object dynamics
Researchers have introduced NEXUS, a novel neural energy-field framework designed to model physically consistent contact-rich 3D object dynamics. Unlike previous methods that often model isolated physical effects, NEXUS…
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New NHODE framework learns physics-informed dynamical systems with unobserved states
Researchers have developed a new framework called neural Hamiltonian ordinary differential equations (NHODE) to learn dynamical systems from data, even when some state variables are unobserved. This approach combines Ha…