Particle-Guided Diffusion for Gas-Phase Reaction Kinetics
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911488781844480 |
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| author | Millard, Andrew Pedersen, Henrik |
| author_facet | Millard, Andrew Pedersen, Henrik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_05139 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Particle-Guided Diffusion for Gas-Phase Reaction Kinetics Millard, Andrew Pedersen, Henrik Chemical Physics Artificial Intelligence Machine Learning 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. |
| title | Particle-Guided Diffusion for Gas-Phase Reaction Kinetics |
| topic | Chemical Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2603.05139 |