推断欠阻尼随机系统的动力学
Inferring the dynamics of underdamped stochastic systems
AI总结：
本文提出了一种基于实验轨迹推断欠阻尼随机系统动力学的方法，适用于单细胞迁移和高维复杂系统，具有抗测量误差能力。
摘要：
许多复杂系统，从迁移细胞到动物群，其随机动力学由欠阻尼 Langevin 方程描述。从实验数据推断此类方程可揭示系统物理规律。本文推导了一个原理性框架，从离散采样且受测量误差影响的实验轨迹中推断欠阻尼随机系统动力学。该框架产生了一种操作方法，即欠阻尼 Langevin 推断（ULI），在单细胞迁移轨迹和复杂高维系统（如具有 Viscek 型对齐相互作用的鸟群）中表现良好。该方法对实验测量误差具有鲁棒性，并包含自洽的推断误差估计。
英文摘要：
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.
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