Statut | Confirmé |
Série | LPTMS |
Domaines | physics |
Date | Mardi 9 Octobre 2018 |
Heure | 11:00 |
Institut | LPTMS |
Salle | LPTMS, salle 201, 2ème étage, Bât 100, Campus d'Orsay |
Nom de l'orateur | Ronceray |
Prenom de l'orateur | Pierre |
Addresse email de l'orateur | |
Institution de l'orateur | Princeton Center for Theoretical Science |
Titre | Learning force fields from stochastic trajectories |
Résumé | 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. |
Numéro de preprint arXiv | |
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