Modelling Gas-Phase Reaction Kinetics with Guided Particle Diffusion Sampling

2026-04-08

arXiv.org

AI Summary

This work addresses the challenge of reconstructing full spatiotemporal trajectories of gaseous reaction kinetics from sparse observational data by proposing a physics-guided diffusion prior sampling method. The approach embeds convection–reaction–diffusion partial differential equations as physical constraints within a diffusion model, enabling, for the first time, high-fidelity reconstruction of continuous and consistent spatiotemporal solutions. Under observation conditions closely mimicking real experimental settings—characterized by extreme data sparsity—the model not only accurately recovers the underlying spatiotemporal dynamics of the reaction process but also demonstrates strong generalization capabilities under unseen parameter regimes, thereby validating its effectiveness and robustness in extrapolating to new regions of the parameter space.

Abstract

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.

Problem

• gas-phase reaction kinetics
• advection-reaction-diffusion equation
• spatiotemporal reconstruction
• time-dependent PDEs
• diffusion sampling

Innovation

• guided diffusion sampling
• advection-reaction-diffusion
• spatiotemporal reconstruction
• physics-guided learning
• reaction kinetics