论文标题
推断失水不足的随机系统的动力学
Inferring the dynamics of underdamped stochastic systems
论文作者
论文摘要
从迁移细胞到动物群，许多复杂的系统都表现出受阻尼不足的兰格文方程描述的随机动力学。从实验数据中推断出这种运动方程式可以为控制系统的物理定律提供深刻的了解。在这里，我们得出了一个原则性的框架，以从现实的实验轨迹中推断失水不足的随机系统的动力学，并在离散时间进行采样并受到测量误差。该框架产生了一种操作方法，阻尼不足的Langevin推理（ULI），该方法在单个迁移细胞和复杂的高维系统的实验轨迹上表现良好，包括具有Viscek样相互作用的羊群。我们的方法对实验测量误差具有鲁棒性，并包括对推理误差的自洽估计。
Many complex systems, ranging from migrating cells to animal groups, exhibit stochastic dynamics described by the underdamped Langevin equation. Inferring such an equation of motion from experimental data can provide profound insight into the physical laws governing the system. Here, we derive a principled framework to infer the dynamics of underdamped stochastic systems from realistic experimental trajectories, sampled at discrete times and subject to measurement errors. This framework yields an operational method, Underdamped Langevin Inference (ULI), which performs well on experimental trajectories of single migrating cells and in complex high-dimensional systems, including flocks with Viscek-like alignment interactions. Our method is robust to experimental measurement errors, and includes a self-consistent estimate of the inference error.
