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

Source: [Kinetic-Mamba: Mamba-Assisted Predictions of Stiff Chemical Kinetics](<https://www.sota2.com/research/paper/6942392745614693628506ac>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-08T05:00:12.103Z

Authors: Additi Pandey, Liang Wei, Hessam Babaee, George Em Karniadakis

Published: 2025-12-16T14:56:53.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2512.14471>)

[PDF](<https://arxiv.org/pdf/2512.14471.pdf>)