Learning Force Fields from Stochastic Trajectories
Learning Force Fields from Stochastic Trajectories
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DOI：
10.1103/physrevx.10.021009
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发表时间：
2020-04-13
影响因子：
12.5
通讯作者：
Ronceray, Pierre
中科院分区：
文献类型：
--
作者：
Frishman, Anna;Ronceray, Pierre
当监测随机系统的动力学时，例如被热噪声搅动的相互作用的粒子，将确定性作用力从布朗运动中分离出来是具有挑战性的。事实上，我们证明了存在一个信息论界限，即当系统被视为通信通道时的容量，它限制了从布朗轨迹中提取关于力场的信息的速率。这一能力为系统的熵产生率提供了一个上界，并量化了轨迹变得与纯布朗运动可区分的速度。我们提出了一种实用和原则性的方法--随机力推理，它利用这些信息来逼近力场和空间可变的扩散系数。它是数据高效的，包括在高维数据中，对实验噪声具有鲁棒性，并提供对推理误差的自洽估计。除了力之外，这项技术还可以很容易地用有限的数据量来评估不平衡电流和相应的熵产生。
When monitoring the dynamics of stochastic systems, such as interacting particles agitated by thermal noise, disentangling deterministic forces from Brownian motion is challenging. Indeed, we show that there is an information-theoretic bound, the capacity of the system when viewed as a communication channel, that limits the rate at which information about the force field can be extracted from a Brownian trajectory. This capacity provides an upper bound to the system's entropy production rate and quantifies the rate at which the trajectory becomes distinguishable from pure Brownian motion. We propose a practical and principled method, stochastic force inference, that uses this information to approximate force fields and spatially variable diffusion coefficients. It is data efficient, including in high dimensions, robust to experimental noise, and provides a self-consistent estimate of the inference error. In addition to forces, this technique readily permits the evaluation of out-of-equilibrium currents and the corresponding entropy production with a limited amount of data.