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 partially observed settings, where some state variables are completely unobserved and must be inferred without direct supervision. Here, we present neural Hamiltonian ordinary differential equations (NHODE), a framework that combines Hamiltonian neural networks (HNNs) with neural ordinary differential equations (neural
The continuous advancements in AI and machine learning, particularly in neural networks and computational physics, are enabling more sophisticated approaches to modeling complex systems.
This development proposes a method to improve the generalization and robustness of AI models in dynamical systems, especially in scenarios with incomplete data, which is crucial for real-world applications.
This research could lead to more accurate and reliable AI systems for predicting the behavior of complex physical systems even when not all variables are directly observable, expanding the applicability of AI in scientific discovery and engineering.
- · AI researchers
- · Robotics
- · Scientific computing
- · Autonomous systems
- · Traditional modeling approaches
- · Systems heavily reliant on full state observation
AI models will become more robust and accurate in learning and simulating physical systems, even with partial observations.
This improved modeling capability will accelerate the development of autonomous systems in complex, real-world environments.
The enhanced understanding and prediction of physical systems could significantly impact fields like material science, climate modeling, and drug discovery by reducing the need for complete experimental data.
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