[Dottorcomp] Fwd (terzo link zoom): [sciences-ljll-seminaire] Ce jeudi 14 décembre de 11h à 12h30 : Mini-cours-3 des Leçons Jacques-Louis Lions d'Andrew Stuart (exposé avec diffusion simultanée par Zoom)

[Lorenzo Tamellini]

Buongiorno a tutt*,

ricevo e inoltro il link zoom per l'ultima lezione del corso qui sotto

Cordiali saluti,
Lorenzo Tamellini


---------- Forwarded message ---------
Da: francois murat <francois.murat a sorbonne-universite.fr>
Date: gio 14 dic 2023 alle ore 02:10
Subject: [sciences-ljll-seminaire] Ce jeudi 14 décembre de 11h à 12h30 :
Mini-cours-3 des Leçons Jacques-Louis Lions d'Andrew Stuart (exposé avec
diffusion simultanée par Zoom)
To: <sciences-ljll-seminaire a listes.sorbonne-universite.fr>


*Ce **jeudi 14 décembre** de 11h à 12h30 : *
*Mini-cours-3 des Leçons Jacques-Louis Lions d'**Andrew Stuart *

(Institut de technologie de Californie (Caltech))


*Identifiants de connexion Zoom **pour le Mini-cours-3 :*

*Date et heure : *Jeudi 14 décembre 2023 Mini-cours-3 11h00-12h30 (heure de
Paris)
*Sujet : *Mini-cours-3 Je 14 12 2023 A. Stuart
*Lien Zoom pour assister à l'exposé :*
https://zoom.us/j/95710296184?pwd=eWZKVGdsRUZidFRwcHFtNGxqWEsyZz09
*ID de réunion : *957 1029 6184
*Code secret : *325479

*Attention* : le lien Zoom sera différent chaque jour.



Données par *Andrew Stuart * (Institut de technologie de Californie
(Caltech))

les *Leçons Jacques-Louis Lions 2023 *consisteront en :


*— un mini-cours intitulé Ensemble Kalman filter : Algorithms, analysis and
applications *
3 séances, les *mardi 12, mercredi 13 et jeudi 14 décembre* 2023 de 11h à
12h30,
Salle du séminaire du Laboratoire Jacques-Louis Lions,
barre 15-16, 3ème étage, salle 09 (15-16-3-09),
Sorbonne Université, Campus Jussieu, 4 place Jussieu, Paris 5ème,


*— et un colloquium intitulé Operator learning : Acceleration and discovery
of computational models *
le *vendredi 15 décembre* 2023 de 14h à 15h,
Amphithéâtre 25,
entrée face à la tour 25, niveau dalle Jussieu,
Sorbonne Université, Campus Jussieu, 4 place Jussieu, Paris 5ème.

*Tous les exposés seront donnés en présence et retransmis en temps réel par
Zoom.*


*Résumé du mini-cours*
*Ensemble Kalman Filter: Algorithms, analysis and applications*

In 1960 Rudolph Kalman [1] published what is arguably the first paper to
develop a systematic, principled approach to the use of data to improve the
predictive capability of dynamical systems. As our ability to gather data
grows at an enormous rate, the importance of this work continues to grow
too. Kalman's paper is confined to linear dynamical systems subject to
Gaussian noise; the work of Geir Evensen [2] in 1994 opened up far wider
applicability of Kalman's ideas by introducing the ensemble Kalman filter.
The ensemble Kalman filter applies to the setting in which nonlinear and
noisy observations are used to make improved predictions of the state of a
Markov chain. The algorithm results in an interacting particle system
combining elements of the Markov chain and the observation process. In
these lectures I will introduce a unifying mean-field perspective on the
algorithm, derived in the limit of an infinite number of interacting
particles. I will then describe how the methodology can be used to study
inverse problems, opening up diverse applications beyond prediction in
dynamical systems. Finally I will describe analysis of the accuracy of the
methodology, both in terms of accuracy and uncertainty quantification;
despite its widespread adoption in applications, a complete mathematical
theory is lacking and there are many opportunities for analysis in this
area.

Lecture 1: The algorithm
Lecture 2: Inverse problems and applications
Lecture 3: Analysis of accuracy and uncertainty quantification

[1] R. Kalman, {A new approach to linear filtering and prediction problems.}
Journal of Basic Engineering, 82:35–45, 1960.
[2] G. Evensen, {Sequential data assimilation with a nonlinear
quasi-geostrophic model using Monte Carlo methods to forecast error
statistics.}
Journal of Geophysical Research: Oceans, 99(C5):10143–10162, 1994.


*Références bibliographiques pour les Leçons Jacques-Louis Lions 2023
(Andrew Stuart)*
https://www.ljll.math.upmc.fr/IMG/pdf/references_biblio_lecons_jacques-louis_lions_2023_andrew_stuart.pdf
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