English(EN) Data-driven Effective Modeling of Stochastic Chemical Reaction Networks

新的数据驱动模型加速了随机化学反应模拟

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-27 04:00

研究人员开发了一种新颖的数据驱动方法，以更有效地模拟随机化学反应网络。该方法利用一个机器学习模型（特别是条件归一化流），该模型在模拟数据上进行训练，以近似底层马尔可夫链的转移核。所得的随机传播器允许在更粗的时间步长下生成统计上一致的轨迹，与传统的精确方法相比，大大降低了计算成本。 AI

影响该方法可以通过实现对复杂生物和化学系统更快、更具成本效益的模拟来加速科学发现。

排序理由详细介绍新计算方法的学术论文。[lever_c_research降级：ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Yuan Chen, Weize Mao, Dongbin Xiu · 2026-08-27 04:00

数据驱动的随机化学反应网络有效建模

arXiv:2608.25421v1 Announce Type: cross 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 …