Status | Confirmed |
Seminar Series | LPTMS |
Subjects | physics |
Date | Tuesday 9 October 2018 |
Time | 11:00 |
Institute | LPTMS |
Seminar Room | LPTMS, salle 201, 2ème étage, Bât 100, Campus d'Orsay |
Speaker's Last Name | Ronceray |
Speaker's First Name | Pierre |
Speaker's Email Address | |
Speaker's Institution | Princeton Center for Theoretical Science |
Title | Learning force fields from stochastic trajectories |
Abstract | From nanometer-scale proteins to micron-scale colloidal particles, particles in biological and soft matter systems undergo Brownian dynamics: their deterministic motion due to external forces and interactions competes with the random diffusion due to thermal noise. In the absence of forces, all trajectories look alike: the key information characterizing the systems dynamics thus lies in its force field. However, reconstructing the force field by inspecting microscopy observations of the systems trajectory is a hard problem, for two reasons. First, there needs to be enough information about the force available in the trajectory: the effect of the force field becomes apparent only after a long enough observation time. Second, one needs a practical method to extract that information and reconstruct the force field, which is challenging for force fields with a spatial structure, in particular in the presence of measurement noise. Here we address these two problems for steady-state Brownian trajectories. We first give a quantitative meaning to the information contained in a trajectory, and show how it limits force inference. We then propose a practical procedure to optimally use this information to reconstruct the force field by decomposing it into moments. Using simple model stochastic processes, we demonstrate that our method permits a quantitative evaluation of phase space forces and currents, circulation, and entropy production with a minimal amount of data. |
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