Learning force fields from stochastic trajectories

Particles in biological and soft matter systems undergo Brownian dynamics: their deterministic motion, induced by forces, competes with random diffusion due to thermal noise. More broadly, Brownian dynamics is a generic and simple model for dynamical systems with fast degrees of freedom modeled as noise.  

Provided only with the time-series of positions of such a system, i.e. a trajectory in phase space, it is challenging to infer what force field had produced it. At the same time, knowledge of the force field is key to characterize the dynamical system. I will discuss the information about the force field contained in the trajectory and present a bound on the rate at which it can be extracted. I will then present a practical method, Stochastic Force Inference, that optimally uses this information to approximate force fields. This technique also permits the evaluation of out-of-equilibrium currents and entropy production, as well as diffusion coefficients.

Zoom link: https://zoom.us/j/98286706234?pwd=bm1JUFVYcTJkaVl1VU55L0FiWDRIUT09