arXiv Machine Learning August 3, 2026 By Jaesung Choi

Extrapolating the emergence of Hamiltonian chaos with random-feature Hamiltonian neural networks

Read the original on arXiv Machine Learning →

arXiv:2607. 28977v1 Announce Type: cross Abstract: Machine learning of Hamiltonian dynamics has driven growing interest in Hamiltonian neural networks (HNNs), which encode Hamilton's equations of motion into the learning architecture.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv Machine Learning

Jun 2

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach

arXiv:2509. 24627v2 Announce Type: replace Abstract: Embedding physical intuition into network architectures allows the learning of dynamics that enforce fundamental properties, such as energy conservation laws, thereby leading to physically-plausible predictions.

By Katharina Friedl, No\'emie Jaquier, Alyx Liao, Danica Kragic

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arXiv Machine Learning

Jun 26

Symplectic Neural Networks for learning Generalized Hamiltonians

arXiv:2606. 27029v1 Announce Type: new Abstract: Hamiltonian Neural Networks (HNNs) integrate physical priors into neural models by learning a system's Hamiltonian, improving generalization and sample efficiency.

By Harsh Choudhary, Vyacheslav Kungurtsev, Chandan Gupta, Melvin Leok, Georgios Korpas

arXiv Machine Learning

Aug 5

Symplectic Neural Networks for Learning Non-Separable Hamiltonians

arXiv:2606. 27029v2 Announce Type: replace Abstract: Hamiltonian Neural Networks (HNNs) integrate physical priors into neural models by learning a system's Hamiltonian, improving generalization and sample efficiency.

By Harsh Choudhary, Vyacheslav Kungurtsev, Chandan Gupta, Melvin Leok, Georgios Korpas

arXiv AI

1d ago

PhyxMamba: Chaotic System Reconstruction from Short Context Observations with Generative State-Space Models

arXiv:2505. 23863v3 Announce Type: replace-cross Abstract: Understanding chaotic dynamics is a fundamental problem across scientific disciplines, including climate science, neuroscience, and fluid dynamics, yet direct experimentation and intervention in such systems are often infeasible.

By Chang Liu, Bohao Zhao, Jingtao Ding, Huandong Wang, Yong Li

rag

arXiv Machine Learning

Aug 12

A matched-integrator evaluation of Hamiltonian neural networks on pendulum and Kepler dynamics

arXiv:2608. 10235v1 Announce Type: new Abstract: Hamiltonian Neural Networks (HNNs) parameterize conservative dynamics through a learned scalar Hamiltonian, providing an architectural prior that is absent from generic vector-field neural networks.

By Lenick Kemunto Nyabuto, Yae Ulrich Gaba, Birahim Tewe

arXiv Machine Learning

Jul 10

Neural non-canonical Hamiltonian dynamics for long-time simulations

arXiv:2510. 01788v2 Announce Type: replace Abstract: This work focuses on learning non-canonical Hamiltonian dynamics from data, where long-term predictions require the preservation of structure both in the learned model and in numerical schemes.

By Cl\'ementine Court\`es (IRMA, MACARON), Emmanuel Franck (MACARON), Michael Kraus (IPP), Laurent Navoret (IRMA, MACARON), L\'eopold Tr\'emant (LML)

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach

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Symplectic Neural Networks for Learning Non-Separable Hamiltonians

PhyxMamba: Chaotic System Reconstruction from Short Context Observations with Generative State-Space Models

A matched-integrator evaluation of Hamiltonian neural networks on pendulum and Kepler dynamics

Neural non-canonical Hamiltonian dynamics for long-time simulations