[2512.14471] Kinetic-Mamba: Mamba-Assisted Predictions of Stiff Chemical Kinetics

Computer Science > Machine Learning arXiv:2512.14471 (cs) [Submitted on 16 Dec 2025 (v1), last revised 5 Apr 2026 (this version, v2)] Title:Kinetic-Mamba: Mamba-Assisted Predictions of Stiff Chemical Kinetics Authors:Additi Pandey, Liang Wei, Hessam Babaee, George Em Karniadakis View a PDF of the paper titled Kinetic-Mamba: Mamba-Assisted Predictions of Stiff Chemical Kinetics, by Additi Pandey and 3 other authors View PDF HTML (experimental) 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...