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arXiv · 2611.01651

Physics-Informed One-Step Generation on Long-short flow maps

Zhiqi Li·Duowen Chen·Jinjin He·Bo Zhu
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Abstract

Physics-informed generative models should produce samples that are both data-aligned and physically valid, yet existing methods are often iterative and too slow for practical use. Achieving this with a one-step flow-map model is difficult because both data-distribution and physics-constraint learning are enforced only indirectly through surrogate objectives, making their joint learning fragile for long-range flow maps. We propose \textbf{Physics-Informed Long-Short Flow Maps with Direct Guidance (LSFM-DG)}, which makes direct target computation feasible by decomposing one-step generation into a long flow map for global evolution and a near-terminal short flow map with computable closed-form solutions and physics potentials. As the short flow map shrinks to zero, it provides both direct distributional and physical targets for learning the long flow map. This yields a unified one-step flow-map model that removes Jensen's gap, improves training stability, and achieves stronger data fidelity and physical consistency without distillation.

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Zhiqi Li, Duowen Chen, Jinjin He, Bo Zhu. 2026-11-01. Physics-Informed One-Step Generation on Long-short flow maps. https://arxiv.org/abs/2611.01651

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