基于数据驱动的随机化学反应网络有效建模
Data-driven Effective Modeling of Stochastic Chemical Reaction Networks
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中文总结 AI 辅助
针对随机化学反应网络的SSA计算成本高的问题，提出基于条件归一化流的数据驱动模型，以粗时间步长生成统计一致轨迹，显著降低计算成本并验证了方法的准确性与效率。
中文摘要 AI 辅助
随机模拟算法（SSA）被广泛认为是随机化学反应网络的精确算法，但存在计算成本高的问题。本研究提出一种数据驱动的有效模型，该模型基于用户定义的粗时间步长运行，不依赖底层微观反应事件尺度，通过在短时间SSA模拟数据上训练的生成机器学习模型，直接近似SSA诱导的连续时间马尔可夫链的有限时间转移核来实现。训练后的模型构建随机传播子，以恒定粗时间步长递归生成统计一致的轨迹，计算成本显著降低。本文采用条件归一化流作为随机传播子，通过大量数值例子验证了所提方法的准确性和效率。
英文摘要
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.
发表机构
- The Ohio State University（俄亥俄州立大学）
机构由 AI 辅助整理，请以论文原文为准。
