[2604.00264] Autonomous Adaptive Solver Selection for Chemistry Integration via Reinforcement Learning

Computer Science > Machine Learning arXiv:2604.00264 (cs) [Submitted on 31 Mar 2026] Title:Autonomous Adaptive Solver Selection for Chemistry Integration via Reinforcement Learning Authors:Eloghosa Ikponmwoba, Opeoluwa Owoyele View a PDF of the paper titled Autonomous Adaptive Solver Selection for Chemistry Integration via Reinforcement Learning, by Eloghosa Ikponmwoba and Opeoluwa Owoyele View PDF HTML (experimental) Abstract:The computational cost of stiff chemical kinetics remains a dominant bottleneck in reacting-flow simulation, yet hybrid integration strategies are typically driven by hand-tuned heuristics or supervised predictors that make myopic decisions from instantaneous local state. We introduce a constrained reinforcement learning (RL) framework that autonomously selects between an implicit BDF integrator (CVODE) and a quasi-steady-state (QSS) solver during chemistry integration. Solver selection is cast as a Markov decision process. The agent learns trajectory-aware policies that account for how present solver choices influence downstream error accumulation, while minimizing computational cost under a user-prescribed accuracy tolerance enforced through a Lagrangian reward with online multiplier adaptation. Across sampled 0D homogeneous reactor conditions, the RL-adaptive policy achieves a mean speedup of approximately $3\times$, with speedups ranging from $1.11\times$ to $10.58\times$, while maintaining accurate ignition delays and species profiles for a 106-...