论文标题
从随机活动粒子动力学中学习确定性流体动力方程
Learning deterministic hydrodynamic equations from stochastic active particle dynamics
论文作者
论文摘要
我们提出了直接从随机非平衡活动粒子轨迹中学习确定性流体动力模型的原则性数据驱动策略。我们将我们的方法应用于学习的流体动力模型，以用于在自propelled粒子系统中观察到的传播密度小道，并学习上皮组织中细胞动力学的连续描述。我们还从随机粒子轨迹推断出潜在的趋化场驱动趋化性。这表明，统计学习理论与物理先验相结合可以实现生命系统中集体运动的非平衡随机过程的多尺度模型。
We present a principled data-driven strategy for learning deterministic hydrodynamic models directly from stochastic non-equilibrium active particle trajectories. We apply our method to learning a hydrodynamic model for the propagating density lanes observed in self-propelled particle systems and to learning a continuum description of cell dynamics in epithelial tissues. We also infer from stochastic particle trajectories the latent phoretic fields driving chemotaxis. This demonstrates that statistical learning theory combined with physical priors can enable discovery of multi-scale models of non-equilibrium stochastic processes characteristic of collective movement in living systems.
