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arXiv 提交日期: 2026-03-05
📄 Abstract - Particle-Guided Diffusion for Gas-Phase Reaction Kinetics

Physics-guided sampling with diffusion model priors has shown promise for solving partial differential equation (PDE) governed problems, but applications to chemically meaningful reaction-transport systems remain limited. We apply diffusion-based guided sampling to gas-phase chemical reactions by training on solutions of the advection-reaction-diffusion (ARD) equation across varying parameters. The method generates physically consistent concentration fields and accurately predicts outlet concentrations, including at unseen parameter values, demonstrating the potential of diffusion models for inference in reactive transport.

顶级标签: systems model training machine learning
详细标签: diffusion models reaction kinetics physics-guided sampling partial differential equations reactive transport 或 搜索:

用于气相反应动力学的粒子引导扩散方法 / Particle-Guided Diffusion for Gas-Phase Reaction Kinetics


1️⃣ 一句话总结

这项研究提出了一种基于扩散模型的引导采样方法,用于高效且准确地模拟和预测气相化学反应中的浓度分布,即使在未训练过的参数条件下也能保持物理一致性。

源自 arXiv: 2603.05139