Problem
Research questions and friction points this paper is trying to address.
Stochastic Chemical Reaction Networks
Computational Cost
Stochastic Simulation Algorithm
Data-driven Effective Modeling of Stochastic Chemical Reaction Networks
🤖 AI Summary
本文提出一种数据驱动的有效模型，通过生成式机器学习直接逼近SSA诱导的连续时间马尔可夫链的有限时间转移核，以解决随机化学反应网络模拟的高计算成本问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
data-driven effective model
coarse time step
generative machine learning model
stochastic propagator
conditional normalizing flow
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