Kinetic-Mamba: Mamba-Assisted Predictions of Stiff Chemical Kinetics

TL;DR

Kinetic-Mamba is a neural operator framework that accurately predicts complex chemical kinetics evolution, integrating Mamba architectures with conservation constraints and regime-specific modeling.

Contribution

It introduces a novel neural operator framework combining Mamba models with conservation and regime-awareness for chemical kinetics prediction.

Findings

High fidelity predictions on Syngas and GRI-Mech 3.0 mechanisms.

Effective extrapolation on out-of-distribution datasets.

Robustness demonstrated through time-decomposition and recursive strategies.

Abstract

Accurate chemical kinetics modeling is essential for combustion simulations, as it governs the evolution of complex reaction pathways and thermochemical states. In this work, we introduce Kinetic-Mamba, a Mamba-based neural operator framework that integrates the expressive power of neural operators with the efficient temporal modeling capabilities of Mamba architectures. The framework comprises three complementary models: (i) a standalone Mamba model that predicts the time evolution of thermochemical state variables from given initial conditions; (ii) a constrained Mamba model that enforces mass conservation while learning the state dynamics; and (iii) a regime-informed architecture employing two standalone Mamba models to capture dynamics across temperature-dependent regimes. We additionally develop a latent Kinetic-Mamba variant that evolves dynamics in a reduced latent space and reconstructs the full state on the physical manifold. The accuracy and robustness of Kinetic-Mamba was evaluated using both time-decomposition and recursive-prediction strategies. We further assess the extrapolation capabilities of the model on varied out-of-distribution datasets. Computational experiments on Syngas and GRI-Mech 3.0 reaction mechanisms demonstrate that our framework achieves high fidelity in predicting complex kinetic behavior using only the initial conditions of the state variables.