BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:MEchanics GAthering -MEGA- Seminar: Talk 1 - Weak nonlinearity for
  strong nonnormality\; Talk 2 - Network-based modeling of turbulent fows
DTSTART:20211021T161500
DTEND:20211021T173000
DTSTAMP:20261005T001412Z
UID:16a1a1c82c5666049b23de3c2b718e4de233bfb60ec4fd232fbdc067
CATEGORIES:Conferences - Seminars
DESCRIPTION:Yves-Marie Ducimetière (LFMI\, EPFL)\, Daniel Fernex (UNFo
 LD\, EPFL)\nTalk 1: Weak nonlinearity for strong nonnormality\, by Yves-M
 arie Ducimetière (LFMI\, EPFL)\n\nAbstract We propose a theoretical app
 roach to derive amplitude equations governing the weakly nonlinear evoluti
 on of nonnormal systems\, when they experience transient growth or respond
  to harmonic forcing. This approach reconciles the non-modal nature of the
 se growth mechanisms and the need for a center manifold to project the lea
 ding-order dynamics. Under the hypothesis of strong nonnormality\, the met
 hodology is outlined for a generic nonlinear dynamical system\, and two ap
 plication cases highlight two common nonnormal mechanisms in hydrodynamics
 : the flow past a backward-facing step\, subject to streamwise convective 
 nonnormal amplification\, and the plane Poiseuille flow\, subject to lift-
 up nonnormality.\n\nBio I am a PhD student at the Laboratory of Fluid Mec
 hanics and Instabilities (LFMI). I am interested in weakly nonlinear effe
 cts on complex systems\, typically fluid flows\, and subject to stochastic
 /harmonic forcing and/or transient growth. \n\nTalk 2: Network-based mode
 ling of turbulent fows\, by Daniel Fernex (UNFoLD\, EPFL)\n\nAbstract C
 omplex nonlinear dynamics govern many fields of science and engineering. D
 ynamic modeling for the long-term features is a key enabler for understand
 ing\, modeling\, prediction\, control\, and optimization. Here we present 
 the cluster-based network modeling bridging machine learning\, network sci
 ence\, and statistical physics. Cluster-based network modeling describes s
 hort- and long-term behavior and is fully automatable\, as it does not rel
 y on application-specific knowledge. This method is demonstrated on numero
 us examples including a high-dimensional boundary layer flow.\n\nBio Dani
 el Fernex is a postdoctoral fellow in the Unsteady Flow Diagnostics Labora
 tory (UNFoLD) at EPFL. During his PhD at the Technical University Braunsch
 weig he developed network-based techniques to derive simple models from hi
 gly non-linear and high-dimensional physical systems. At EPFL\, he extends
  his methods to the field of unsteady aerodynamics to model\, understand a
 nd optimize systems such as vertical axis wind turbines and pitching airfo
 ils.
LOCATION:MED 0 1418 https://plan.epfl.ch/?room==MED%200%201418 https://epf
 l.zoom.us/j/67873367071?pwd=b0NEeWY2MFJqNGUzUitJV256YSt6QT09
STATUS:CONFIRMED
END:VEVENT
END:VCALENDAR