Titre :

Coupling rare event algorithms with data-based learned committor functions using the analogue Markov chain

Auteur(s) :

Lucente, Dario [Auteur]
Laboratoire de Physique de l'ENS Lyon [Phys-ENS]
Rolland, Joran [Auteur]
Laboratoire de Mécanique des Fluides de Lille - Kampé de Fériet [LMFL]
Herbert, Corentin [Auteur]
Laboratoire de Physique de l'ENS Lyon [Phys-ENS]
Bouchet, Freddy [Auteur]
Laboratoire de Physique de l'ENS Lyon [Phys-ENS]

Titre de la revue :

Journal of Statistical Mechanics: Theory and Experiment

Pagination :

083201

Éditeur :

IOP Publishing

Date de publication :

2022-08-10

ISSN :

1742-5468

Mot(s)-clé(s) en anglais :

machine learning
nonlinear dynamics
statistical mechanics of geophysical systems
stochastic processes

Discipline(s) HAL :

Physique [physics]/Matière Condensée [cond-mat]/Mécanique statistique [cond-mat.stat-mech]

Résumé en anglais : [en]

Rare events play a crucial role in many physics, chemistry, and biology phenomena, when they change the structure of the system, for instance in the case of multistability, or when they have a huge impact. Rare event ...
Lire la suite >Rare events play a crucial role in many physics, chemistry, and biology phenomena, when they change the structure of the system, for instance in the case of multistability, or when they have a huge impact. Rare event algorithms have been devised to simulate them efficiently, avoiding the computation of long periods of typical fluctuations. We consider here the family of splitting or cloning algorithms, which are versatile and specifically suited for far-from-equilibrium dynamics. To be efficient, these algorithms need to use a smart score function during the selection stage. Committor functions are the optimal score functions. In this work we propose a new approach, based on the analogue Markov chain, for a data-based learning of approximate committor functions. We demonstrate that such learned committor functions are extremely efficient score functions when used with the Adaptive Multilevel Splitting algorithm. We illustrate our approach for a gradient dynamics in a three-well potential, and for the Charney-DeVore model, which is a paradigmatic toy model of multistability for atmospheric dynamics. For these two dynamics, we show that having observed a few transitions is enough to have a very efficient data-based score function for the rare event algorithm. This new approach is promising for use for complex dynamics: the rare events can be simulated with a minimal prior knowledge and the results are much more precise than those obtained with a user-designed score function.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Laboratoire de Mécanique des Fluides de Lille Kampé de Fériet (LMFL) - UMR 9014

Source :

Harvested from HAL