Statistical prediction of extreme events from small datasets

Autor:	Racca, Alberto, Magri, Luca
Rok vydání:	2022
Předmět:	Physics - Fluid Dynamics Computer Science - Machine Learning Nonlinear Sciences - Chaotic Dynamics
Druh dokumentu:	Working Paper
Popis:	We propose Echo State Networks (ESNs) to predict the statistics of extreme events in a turbulent flow. We train the ESNs on small datasets that lack information about the extreme events. We asses whether the networks are able to extrapolate from the small imperfect datasets and predict the heavy-tail statistics that describe the events. We find that the networks correctly predict the events and improve the statistics of the system with respect to the training data in almost all cases analysed. This opens up new possibilities for the statistical prediction of extreme events in turbulence.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2201.08294 Zobrazit plný text záznamu View this record from Arxiv