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Statut |
Confirmé |
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Série |
SEM-LPTMC |
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Domaines |
cond-mat |
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Date |
Lundi 24 Janvier 2022 |
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Heure |
10:45 |
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Institut |
LPTMC |
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Salle |
online |
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Nom de l'orateur |
Vroylandt |
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Prenom de l'orateur |
Hadrien |
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Addresse email de l'orateur |
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Institution de l'orateur |
Institut des Sciences du Calcul et des Données, Sorbonne Université |
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Titre |
Learning the dynamics of systems with memory: Generalized Langevin equations |
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Résumé |
Generalized Langevin equations with non-linear forces and memory kernels are
commonly used to describe the effective dynamics of coarse-grained variables in
molecular dynamics. Such reduced dynamics play an essential role in the study of
a broad class of processes, ranging from chemical reactions in solution to
conformational changes in biomolecules or phase transitions in condensed matter
systems. I will first discuss the derivation of the generalized Langevin
equations, emphasizing the need for memory in the effective dynamics due to the
lack of a proper separation of time scales. Then, I will turn on the inference
of such generalized Langevin equations from observed trajectories, using a
maximum likelihood approach. This data-driven approach provides a reduced
dynamical model for collective variables, enabling the accurate sampling of
their long-time dynamical properties at a computational cost drastically reduced
with respect to all-atom numerical simulations. I will illustrate the potential
of this method on several model systems, both in and out of equilibrium. |
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Numéro de preprint arXiv |
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Commentaires |
Zoom: https://us06web.zoom.us/j/81533150574?pwd=NlM1ZlJvUTQvZUxpVmV6QWdMbmlLQT09
Meeting ID: 815 3315 0574
Passcode: 336254 |
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Fichiers attachés |
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