[2603.05139] Particle-Guided Diffusion for Gas-Phase Reaction Kinetics

[2603.05139] Particle-Guided Diffusion for Gas-Phase Reaction Kinetics

arXiv - Machine Learning 3 min read

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Abstract page for arXiv paper 2603.05139: Particle-Guided Diffusion for Gas-Phase Reaction Kinetics

Physics > Chemical Physics arXiv:2603.05139 (physics) [Submitted on 5 Mar 2026] Title:Particle-Guided Diffusion for Gas-Phase Reaction Kinetics Authors:Andrew Millard, Henrik Pedersen View a PDF of the paper titled Particle-Guided Diffusion for Gas-Phase Reaction Kinetics, by Andrew Millard and 1 other authors View PDF HTML (experimental) Abstract: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. Subjects: Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2603.05139 [physics.chem-ph]   (or arXiv:2603.05139v1 [physics.chem-ph] for this version)   https://doi.org/10.48550/arXiv.2603.05139 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Andrew Millard Mr [view email] [v1] Thu, 5 Mar 2026 13:09:28 UTC (2,634 KB) Full-text links: Access Paper: View a PDF of the paper titled Particle-Guided Diffusion ...

Originally published on March 06, 2026. Curated by AI News.

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