MPINeuralODE: Multiple-Initial-Condition Physics-Informed Neural ODEs for Globally Consistent Dynamical System Learning
arXiv:2605.13305v2 Announce Type: replace Abstract: Neural ordinary differential equations (Neural ODEs) often fit training trajectories while generalizing poorly to unseen initial conditions and long horizons. We propose MPINeuralODE, which combines a soft physics-informed residual with a Multiple-Initial-Condition (MIC) multiple-shooting curriculum whose ingredients are structurally complementary: the physics term anchors the vector-field magnitude on the support that MIC enlarges. We evaluate along three axes: out-of-sample error, long-horizon stability, and Hamiltonian drift, which togethe
This research addresses a known limitation of Neural ODEs in generalizing to new conditions, reflecting a current focus in AI on robust and trustworthy model performance.
Improved generalization and stability in AI models for complex dynamical systems are critical for reliable simulation, prediction, and control in various scientific and engineering applications.
This paper introduces a method to make AI models of dynamic systems more robust and less prone to errors with unseen data or over long simulation periods, enhancing their practical utility.
- · AI researchers
- · Scientific simulation platforms
- · Chemical engineering
- · Physics modeling
- · Traditional, less robust Neural ODE implementations
More accurate and stable AI-driven simulations of physical and chemical processes become feasible.
This could accelerate discovery cycles in materials science, drug development, and climate modeling by enabling more reliable predictive models.
Long-term, improved dynamical system learning might contribute to the development of highly autonomous AI agents capable of understanding and interacting with complex real-world environments more effectively.
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Read at arXiv cs.LG