标题： 无需仿真的随机动力学结构学习 显示英文标题

标题： Simulation-free Structure Learning for Stochastic Dynamics

摘要： 对动态系统进行建模并揭示其潜在的因果关系是自然科学许多领域的核心问题。 各种物理系统，如细胞生物学中出现的系统，本质上是高维且具有随机性的，并且只能获得部分、噪声状态测量值。 这给解决建模潜在动力学和推断这些系统网络结构的问题带来了重大挑战。 现有方法通常专门用于结构学习或在群体层面建模动力学，但在同时处理这两个问题方面存在局限性。 在本工作中，我们同时解决了这两个问题：我们提出了StructureFlow，这是一种新颖且有原则的无模拟方法，用于联合学习物理系统的结构和随机群体动力学。 我们展示了StructureFlow在从干预中进行结构学习以及条件群体动力学的动态（轨迹）推断任务中的实用性。 我们在高维合成系统、一组生物合理模拟系统和一个实验性单细胞数据集上对我们的方法进行了实证评估。 我们证明，StructureFlow可以在同时建模其条件群体动力学的同时学习潜在系统的结构——这是实现系统行为机制理解的关键一步。 显示英文摘要

摘要： Modeling dynamical systems and unraveling their underlying causal relationships is central to many domains in the natural sciences. Various physical systems, such as those arising in cell biology, are inherently high-dimensional and stochastic in nature, and admit only partial, noisy state measurements. This poses a significant challenge for addressing the problems of modeling the underlying dynamics and inferring the network structure of these systems. Existing methods are typically tailored either for structure learning or modeling dynamics at the population level, but are limited in their ability to address both problems together. In this work, we address both problems simultaneously: we present StructureFlow, a novel and principled simulation-free approach for jointly learning the structure and stochastic population dynamics of physical systems. We showcase the utility of StructureFlow for the tasks of structure learning from interventions and dynamical (trajectory) inference of conditional population dynamics. We empirically evaluate our approach on high-dimensional synthetic systems, a set of biologically plausible simulated systems, and an experimental single-cell dataset. We show that StructureFlow can learn the structure of underlying systems while simultaneously modeling their conditional population dynamics -- a key step toward the mechanistic understanding of systems behavior.