用物理引导采样重建气相反应的完整时空演化过程。
Modelling Gas-Phase Reaction Kinetics with Guided Particle Diffusion Sampling
- 结合扩散先验与物理规律，实现对时变方程的精准采样。
- 成功恢复全时空轨迹，且可泛化到未见参数条件。
- 适合需要真实实验模拟的化学动力学研究者。
基于物理引导的采样方法结合扩散先验，在从稀疏观测中求解复杂偏微分方程（PDE）方面表现出色。然而，现有方法多在基准问题上评估，难以充分展现其生成时变PDE连续解的能力，常仅关注单一时刻的重构。本文将该方法应用于由对流-反应-扩散（ARD）方程描述的气相反应动力学问题，更贴近真实实验室实验场景。结果表明，该方法能有效重建完整的时空轨迹，而非孤立状态；同时展现出对未曾见过的参数区域的良好泛化能力，凸显其在实际应用中的潜力。
原文摘要 · Abstract (English)
Physics-guided sampling with diffusion priors has recently shown strong performance in solving complex systems of partial differential equations (PDEs) from sparse observations. However, these methods are typically evaluated on benchmark problems that do not fully demonstrate their ability to generate temporally consistent solutions of time-dependent PDEs, often focusing instead on reconstructing a single snapshot. In this work, we apply these methods to gas-phase reaction kinetics problems governed by the advection-reaction-diffusion (ARD) equation, providing a setting that more closely reflects realistic laboratory experiments. We demonstrate that guided sampling can be used to reconstruct full spatiotemporal trajectories, rather than isolated states. Furthermore, we show that these methods generalise to previously unseen parameter regimes, highlighting their potential for real-world applications.
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