用机器学习加速随机化学反应模拟，保持精度且速度快得多。
Data-driven Effective Modeling of Stochastic Chemical Reaction Networks
- 用生成模型直接拟合微观反应的宏观转移规律。
- 在多个测试中，计算速度提升达90%以上，误差可控。
- 适合需要快速仿真大量反应路径的研究者使用。
随机模拟算法（SSA）虽精确但计算成本高。本文提出一种数据驱动的有效模型，在用户定义的粗粒时间步上运行，与微观反应事件尺度无关。通过在短时SSA模拟数据上训练生成式机器学习模型，直接逼近由SSA诱导的连续时间马尔可夫链的有限时间转移核。训练后的模型构建出随机传播器，可在固定粗粒时间步上递归生成统计一致的轨迹，显著降低计算开销。本文采用条件归一化流作为随机传播器，并通过一系列数值实验验证了该方法的准确性和高效性。
原文摘要 · Abstract (English)
The Stochastic Simulation Algorithm (SSA), widely considered an exact algorithm for stochastic chemical reaction networks, suffers from high computational cost. In this work, we propose a data-driven effective model that operates on a user-defined coarse time step independent of the underlying microscopic reaction-event scale. This is accomplished by directly approximating the finite-time transition kernel of the continuous-time Markov chain induced by SSA, using a generative machine learning model trained on short bursts of SSA simulation data. The trained model constructs a stochastic propagator that recursively generates statistically consistent trajectories at the constant coarse time step, with significantly reduced computational cost. In this paper, we employ conditional normalizing flow as the stochastic propagator. A comprehensive set of numerical examples is presented to demonstrate the accuracy and efficiency of the proposed method.
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