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New NHODE framework learns physics-informed dynamical systems with unobserved states

By PulseAugur Editorial · [2 sources] ·

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 Hamiltonian neural networks with neural ODEs, embedding physical structures like energy conservation to improve generalization and stability. The NHODE framework was tested on various systems, including the chaotic three-body problem, demonstrating superior accuracy and long-horizon prediction capabilities compared to purely data-driven methods. AI

IMPACT This framework could enable more robust AI models for scientific discovery by handling systems with incomplete data.

RANK_REASON The cluster contains an academic paper detailing a new modeling framework.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Sunniva Meltzer, S{\o}lve Eidnes, Alexander Johannes Stasik ·

    Learning partially observed systems with neural Hamiltonian ordinary differential equations

    arXiv:2605.23510v1 Announce Type: new Abstract: When learning dynamical systems from data, embedding physical structure can constrain the solution space and improve generalization, but many physics-informed models assume access to the full system state. This limits their use in p…

  2. arXiv cs.LG TIER_1 English(EN) · Alexander Johannes Stasik ·

    Learning partially observed systems with neural Hamiltonian ordinary differential equations

    When learning dynamical systems from data, embedding physical structure can constrain the solution space and improve generalization, but many physics-informed models assume access to the full system state. This limits their use in partially observed settings, where some state var…

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