mlsanityData to 7 Oct 2026

Data-driven Effective Modeling of Stochastic Chemical Reaction Networks

arXiv 2608.25421 ↗First posted 26 Aug 2026math.NA cs.LG cs.NAcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 51 references that are in the map. First posted on arXiv 26 Aug 2026.

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References · 21

  1. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  2. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,806
  3. 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 1,965
  4. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  5. 2018Data Driven Governing Equations Approximation Using Deep Neural Networkscited by 94
  6. 2018Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systemscited by 88
  7. 2020Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networkscited by 52
  8. 2021Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learningcited by 37
  9. 2021Computational Graph Completioncited by 28
  10. 2023Learning Stochastic Dynamical System via Flow Map Operatorcited by 24
  11. 2018Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matchingcited by 23
  12. 2024A Training-Free Conditional Diffusion Model for Learning Stochastic Dynamical Systemscited by 19
9 more
  1. 2023Flow Map Learning for Unknown Dynamical Systems: Overview, Implementation, and Benchmarkscited by 18
  2. 2022One-Shot Learning of Stochastic Differential Equations with Data Adapted Kernelscited by 15
  3. 2022MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problemscited by 13
  4. 2023Modeling Unknown Stochastic Dynamical System via Autoencodercited by 12
  5. 2022Neural-network solutions to stochastic reaction networkscited by 8
  6. 2024Modeling Unknown Stochastic Dynamical System Subject to External Excitationcited by 7
  7. 2024An efficient Wasserstein-distance approach for reconstructing jump-diffusion processes using parameterized neural networkscited by 5
  8. 2024Data-driven Effective Modeling of Multiscale Stochastic Dynamical Systemscited by 5
  9. 2025Targeted Digital Twin via Flow Map Learning and Its Application to Fluid Dynamicscited by 5

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