BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//wp-events-plugin.com//7.3.5//EN
TZID:Europe/Paris
X-WR-TIMEZONE:Europe/Paris
BEGIN:VEVENT
UID:1151@cran.univ-lorraine.fr
DTSTART;TZID=Europe/Paris:20251118T140000
DTEND;TZID=Europe/Paris:20251118T150000
DTSTAMP:20251107T074044Z
URL:https://www.cran.univ-lorraine.fr/events/seminaire-de-fahim-shakib-tu-
 eindhoven-pays-bas/
SUMMARY:Séminaire de Fahim Shakib (TU Eindhoven\, Pays-Bas)
DESCRIPTION:- Titre : Nonlinear k-Moments: New Tools for Analysis and Desi
 gn of Nonlinear Systems\n\n- Résumé : Despite the central role of momen
 ts in nonlinear model reduction\, the interpretation of the k-moment in no
 nlinear moment matching remains elusive\, unlike the well-established line
 ar time-invariant case. We address this fundamental gap by providing\, for
  the first time\, a concrete and rigorous interpretation of the k-moment f
 or nonlinear systems. Our approach builds on a regular perturbation expans
 ion of the solution to the partial differential equation that characterize
 s the standard nonlinear moment function. By introducing a perturbed signa
 l generator—-comprising a nominal vector field\, a dimensionless perturb
 ation parameter\, and a departure mapping—-we characterize k-moments as 
 higher-order perturbation terms\, offering a time-domain interpretation fo
 r nonlinear systems. This approach enables more accurate moment matching a
 nd improved robustness to small perturbations in the matching signal. Then
 \, with this machinery at hand\, we introduce a novel family of reduced-or
 der models capable of matching the k-moments of the underlying system\, in
  both linear and nonlinear contexts. To validate our theory and demonstrat
 e the advantages of matching k-moments\, we provide examples that construc
 t reduced-order models with reduced sensitivity to small input perturbatio
 ns. Furthermore\, we illustrate how the notion of k-moment can be extended
  beyond model reduction\, for instance to support the design of robust reg
 ulators.\n\n- Biographie : Fahim Shakib received his M.Sc. degree (cum la
 ude) in Mechanical Engineering in 2017 from the Eindhoven University of Te
 chnology\, Eindhoven\, the Netherlands\, where he also received his Ph.D. 
 degree (cum laude) in 2022 for his thesis entitled ‘Data-driven modeling
  and complexity reduction for nonlinear systems with stability guarantees
 ’. In 2023\, he worked at Thermo Fisher Scientific in Eindhoven\, the Ne
 therlands\, before joining the Control and Power Group in the Department o
 f Electrical and Electronic Engineering at Imperial College London\, UK\, 
 as a Research Associate. Since 2025\, he holds an Assistant Professor posi
 tion in the Department of Mechanical Engineering at TU/e. His research int
 erests include system identification\, model reduction\, and control of no
 nlinear systems with a core focus on the integration of physics-based appr
 oaches and data-based approaches.\n- Lien Teams : cliquer ici.
CATEGORIES:Séminaire projet MODELE
LOCATION:CRAN - ENSEM\, 2\, Avenue de la Foret de Haye\, Voandoeuvre-les-Na
 ncy\, 54516\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=2\, Avenue de la Foret de H
 aye\, Voandoeuvre-les-Nancy\, 54516\, France;X-APPLE-RADIUS=100;X-TITLE=CR
 AN - ENSEM:geo:0,0
END:VEVENT
BEGIN:VTIMEZONE
TZID:Europe/Paris
X-LIC-LOCATION:Europe/Paris
BEGIN:STANDARD
DTSTART:20251026T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
END:STANDARD
END:VTIMEZONE
END:VCALENDAR