Autonomous Adaptive Solver Selection for Chemistry Integration via Reinforcement Learning

2026-03-31

AI Summary

This work addresses the high computational cost of integrating stiff chemical kinetics in reactive flow simulations by formulating solver selection as a Markov decision process. The authors propose a trajectory-aware constrained reinforcement learning framework that autonomously switches between the CVODE and QSS solvers. Leveraging a Lagrangian reward mechanism with online multiplier adaptation, the method achieves globally optimal scheduling while satisfying user-specified accuracy constraints. Evaluated on a 0D reactor, the approach yields an average speedup of 3× (up to 10.58×) with only ~1% inference overhead. Notably, it generalizes to 1D diffusion flames without retraining, delivering a stable 2.2× acceleration while preserving high-fidelity temperature fields and species evolution.

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-species \textit{n}-dodecane mechanism and adding approximately $1\%$ inference overhead. Without retraining, the 0D-trained policy transfers to 1D counterflow diffusion flames over strain rates $10$--$2000~\mathrm{s}^{-1}$, delivering consistent $\approx 2.2\times$ speedup relative to CVODE while preserving near-reference temperature accuracy and selecting CVODE at only $12$--$15\%$ of space-time points. Overall, the results demonstrate the potential of the proposed reinforcement learning framework to learn problem-specific integration strategies while respecting accuracy constraints, thereby opening a pathway toward adaptive, self-optimizing workflows for multiphysics systems with spatially heterogeneous stiffness.

Problem

• stiff chemical kinetics
• reacting-flow simulation
• solver selection
• computational cost
• accuracy constraints

Innovation

• reinforcement learning
• adaptive solver selection
• chemical kinetics integration
• constrained optimization
• transferability