从随机轨迹学习力场
Learning force fields from stochastic trajectories
摘要：
当监测随机系统的动力学时，例如由热噪声搅动的相互作用的粒子，从布朗运动中分离确定性力是具有挑战性的。事实上，我们表明，有一个信息理论界，系统的能力，当被视为一个通信信道，限制了有关力场的信息可以从布朗轨迹中提取的速率。这种能力为系统的熵产生率提供了一个上限，并量化了轨迹变得与纯布朗运动不同的速率。我们提出了一个实用的和原则性的方法，随机力推断，使用这些信息近似力场和空间可变的扩散系数。它是数据有效的，包括在高维中，对实验噪声具有鲁棒性，并且提供了推理误差的自洽估计。除了力，这种技术很容易允许评估的平衡电流和相应的熵产生与有限的数据量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
