<div dir="ltr"><div>Buongiorno a tutt*,</div><div><br></div><div>ricevo e inoltro il reminder qui sotto</div><div><br></div><div>Cordiali saluti,</div><div>Lorenzo Tamellini<br></div><div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">---------- Forwarded message ---------<br>Da: <span dir="auto"><<a href="mailto:francois.murat@sorbonne-universite.fr">francois.murat@sorbonne-universite.fr</a>></span><br>Date: mar 12 dic 2023 alle ore 01:06<br>Subject: [sciences-ljll-seminaire] Ce mardi 12 décembre de 11h à 12h30 : Mini-cours-1 des Leçons Jacques-Louis Lions d'Andrew Stuart (exposé avec diffusion simultanée par Zoom)<br>To: <<a href="mailto:sciences-ljll-seminaire@listes.sorbonne-universite.fr">sciences-ljll-seminaire@listes.sorbonne-universite.fr</a>><br></div><br><br><div style="word-wrap:break-word;line-break:after-white-space"><div dir="auto" style="word-wrap:break-word;line-break:after-white-space"><div style="margin:0px;font-stretch:normal;line-height:normal;font-family:"Helvetica Neue";color:rgba(0,0,0,0.85)"><div style="margin:0px;font-stretch:normal;line-height:normal"><span style="font-kerning:none"><b>Ce mardi 12 décembre de 11h à 12h30 : </b></span></div><div style="margin:0px;font-stretch:normal;line-height:normal"><span style="font-kerning:none"><b>Mini-cours-1 des Leçons Jacques-Louis Lions d'Andrew Stuart </b></span></div><p style="margin:0px 0px 4.4px;text-align:justify;font-stretch:normal;line-height:normal"><span style="font-kerning:none">(Institut de technologie de Californie (Caltech))</span></p><p style="margin:0px 0px 4.4px;text-align:justify;font-stretch:normal;line-height:normal;min-height:16px"><span style="font-kerning:none"></span><br></p><div style="margin:0px;text-align:justify;font-stretch:normal;line-height:normal"><span style="font-kerning:none"><b>Identifiants de connexion Zoom pour le </b></span><span style="font-kerning:none"><b>Mini-cours-1 :</b></span></div><div style="margin:0px;text-align:justify;font-stretch:normal;line-height:normal;min-height:16px"><span style="font-kerning:none"></span><br></div><div style="margin:0px;text-align:justify;font-stretch:normal;line-height:normal"><span style="font-kerning:none"><b>Date et heure : </b>Mardi 12 décembre 2023 Mini-cours-1 11h00-12h30 (heure de Paris)</span></div><div style="margin:0px;text-align:justify;font-stretch:normal;line-height:normal"><span style="font-kerning:none">Sujet : Mini-cours-1 Ma 12 12 2023 A. Stuart</span></div><div style="margin:0px;text-align:justify;font-stretch:normal;line-height:normal"><span style="font-kerning:none"><b>Lien Zoom pour assister à l'exposé :</b></span></div><div style="margin:0px;text-align:justify;font-stretch:normal;line-height:normal"><span style="font-kerning:none"><a href="https://zoom.us/j/98722283314?pwd=N1hEZFlzRkwwQXFCZVVDMkNlcVlBQT09" target="_blank">https://zoom.us/j/98722283314?pwd=N1hEZFlzRkwwQXFCZVVDMkNlcVlBQT09</a></span></div><div style="margin:0px;text-align:justify;font-stretch:normal;line-height:normal"><span style="font-kerning:none"><b>ID de réunion : </b>987 2228 3314</span></div><div style="margin:0px;text-align:justify;font-stretch:normal;line-height:normal"><span style="font-kerning:none"><b>Code secret : </b>944387</span></div><div style="margin:0px;text-align:justify;font-stretch:normal;line-height:normal;min-height:16px"><span style="font-kerning:none"></span><br></div><p style="margin:0px 0px 4.4px;text-align:justify;font-stretch:normal;line-height:normal"><span style="font-kerning:none"><b>Attention</b> : le lien Zoom sera différent chaque jour.</span></p><p style="margin:0px 0px 4.4px;text-align:justify;font-stretch:normal;line-height:normal;min-height:16px"><span style="font-kerning:none"></span><br></p><p style="margin:0px 0px 4.4px;text-align:justify;font-stretch:normal;line-height:normal;min-height:16px"><span style="font-kerning:none"></span><br></p><p style="margin:0px 0px 4.4px;text-align:justify;font-stretch:normal;line-height:normal"><span style="font-kerning:none">Données par </span><span style="font-kerning:none"><b>Andrew Stuart </b> </span><span style="font-kerning:none">(Institut de technologie de Californie (Caltech))</span></p><p style="margin:0px 0px 4.4px;text-align:justify;font-stretch:normal;line-height:normal"><span style="font-kerning:none">les <b>Leçons Jacques-Louis Lions 2023 </b>consisteront en : </span></p><p style="margin:0px 0px 4.4px;text-align:justify;font-stretch:normal;line-height:normal"><span style="font-kerning:none"><b>— un mini-cours intitulé <br>
Ensemble Kalman filter : Algorithms, analysis and applications </b><br>
3 séances, les <b>mardi 12, mercredi 13 et jeudi 14 décembre</b> 2023 de 11h à 12h30, <br>
Salle du séminaire du Laboratoire Jacques-Louis Lions, <br>
barre 15-16, 3ème étage, salle 09 (15-16-3-09), <br>
Sorbonne Université, Campus Jussieu, 4 place Jussieu, Paris 5ème, </span></p><p style="margin:0px 0px 4.4px;text-align:justify;font-stretch:normal;line-height:normal"><span style="font-kerning:none"><b>— et un colloquium intitulé <br>
Operator learning : Acceleration and discovery of computational models </b><br>
le <b>vendredi 15 décembre</b> 2023 de 14h à 15h, <br>
Amphithéâtre 25, <br>
entrée face à la tour 25, niveau dalle Jussieu,<br>
Sorbonne Université, Campus Jussieu, 4 place Jussieu, Paris 5ème. </span></p><div style="margin:0px;font-stretch:normal;line-height:normal;min-height:16px"><span style="font-kerning:none"></span><br></div><p style="margin:0px 0px 4.4px;text-align:justify;font-stretch:normal;line-height:normal"><span style="font-kerning:none"><b>Tous les exposés seront donnés en présence et retransmis en temps réel par Zoom.</b></span></p><div style="margin:0px;text-align:justify;font-stretch:normal;line-height:normal;min-height:16px"><span style="font-kerning:none"></span><br></div><div style="margin:0px;font-stretch:normal;line-height:normal;min-height:16px"><span style="font-kerning:none"></span><br></div><div style="margin:0px;font-stretch:normal;line-height:normal"><span style="font-kerning:none"><b>Résumé du mini-cours</b></span></div><div style="margin:0px;font-stretch:normal;line-height:normal"><span style="font-kerning:none"><b>Ensemble Kalman Filter: Algorithms, analysis and applications</b></span></div><div style="margin:0px;font-stretch:normal;line-height:normal;min-height:16px"><span style="font-kerning:none"></span><br></div><div style="margin:0px;font-stretch:normal;line-height:normal"><span style="font-kerning:none">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. </span></div><div style="margin:0px;font-stretch:normal;line-height:normal"><span style="font-kerning:none">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.</span></div><div style="margin:0px;font-stretch:normal;line-height:normal;min-height:16px"><span style="font-kerning:none"></span><br></div><div style="margin:0px;font-stretch:normal;line-height:normal"><span style="font-kerning:none">Lecture 1: The algorithm</span></div><div style="margin:0px;font-stretch:normal;line-height:normal"><span style="font-kerning:none">Lecture 2: Inverse problems and applications</span></div><div style="margin:0px;font-stretch:normal;line-height:normal"><span style="font-kerning:none">Lecture 3: Analysis of accuracy and uncertainty quantification</span></div><div style="margin:0px;font-stretch:normal;line-height:normal;min-height:16px"><span style="font-kerning:none"></span><br></div><div style="margin:0px;font-stretch:normal;line-height:normal"><span style="font-kerning:none">[1] R. Kalman, {A new approach to linear filtering and prediction problems.}</span></div><div style="margin:0px;font-stretch:normal;line-height:normal"><span style="font-kerning:none">Journal of Basic Engineering, 82:35–45, 1960.</span></div><div style="margin:0px;font-stretch:normal;line-height:normal"><span style="font-kerning:none">[2] G. Evensen, {Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics.}</span></div><div style="margin:0px;font-stretch:normal;line-height:normal"><span style="font-kerning:none">Journal of Geophysical Research: Oceans, 99(C5):10143–10162, 1994.</span></div></div><br><br></div></div></div></div></div>