这篇论文用强化学习来优化反应器网络预测贫油熄火，比传统k-means更准还快，适合搞燃烧仿真的人看看。
该研究提出一个强化学习框架，用于优化液态燃料反应器网络，提高贫油熄火(LBO)预测精度。现有方法依赖人工启发式或输入空间距离度量，而该方法是目标导向的，在聚类形成时明确考虑LBO预测准确率。框架采用多阶段聚类-分类策略：先k-means生成大量均匀微簇，再由演员-评论家RL代理合并为最优反应器区域。使用Jet-A机理(119种物种，841个反应)验证，RL框架比k-means具有更好预测保真度，捕捉正确LBO趋势，并实现高保真模型的大幅加速。该RL驱动方法作为计算高效的降阶建模技术，可补充高保真仿真用于快速设计空间探索。
A Reinforcement-Learning-Augmented Liquid-Fueled Reactor Network Model for Predicting Lean Blowout in Gas Turbine Combustors
This study introduces a reinforcement learning (RL) framework for generating optimal liquid-fueled reactors to improve lean blowout (LBO) predictions in gas turbine combustors. Existing approaches for determining cluster boundaries rely on manual heuristics or distance-based metrics in the input space. In contrast, the proposed method is goal-oriented, explicitly accounting for the target metric (e.g., LBO prediction accuracy) during cluster formation. The framework employs a multi-stage clustering--classification strategy: an initial clustering step (e.g., $k$-means clustering) generates a large set of homogeneous micro-clusters, followed by an actor-critic RL agent that merges them into optimal reactor zones. The validation study, performed using a Jet-A mechanism (119 species, 841 reactions), shows the RL framework offers improved predictive fidelity compared to $k$-means and captures the correct LBO trends, while achieving substantial speedups relative to the high-fidelity computational model. Overall, the RL-driven approach demonstrates strong potential as a computationally efficient reduced-order modeling technique that can complement high-fidelity simulations for rapid design-space exploration.