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This paper addresses the computational inefficiencies of the Stochastic Simulation Algorithm (SSA) for stochastic chemical reaction networks by introducing a data-driven model that approximates the transition kernel of the underlying Markov chain. By leveraging a generative machine learning model trained on short bursts of SSA data, the authors create a stochastic propagator that generates statistically consistent trajectories at a user-defined coarse time step. The results show that this approach significantly reduces computational costs while maintaining accuracy, as demonstrated through a series of numerical examples.
A generative model can slash the computational cost of simulating stochastic chemical reactions while preserving accuracy.
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.
