Learning force fields from stochastic trajectories

TL;DR

This paper introduces a new method called Stochastic Force Inference that estimates force fields and diffusion coefficients from stochastic trajectories, leveraging information theory to bound and improve inference accuracy in noisy, high-dimensional systems.

Contribution

It presents a novel, data-efficient approach grounded in information theory for inferring forces and diffusion in stochastic systems, including high-dimensional and noisy data.

Findings

The method achieves high accuracy in force inference from limited data.

It provides a self-consistent estimate of inference error.

It enables evaluation of entropy production and out-of-equilibrium currents.

Abstract

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.