{"id":239,"date":"2023-02-10T18:16:38","date_gmt":"2023-02-10T17:16:38","guid":{"rendered":"https:\/\/project.inria.fr\/ifpen\/?page_id=239"},"modified":"2026-04-01T10:45:55","modified_gmt":"2026-04-01T08:45:55","slug":"projects","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/ifpen\/projects\/","title":{"rendered":"Projects"},"content":{"rendered":"\n
No<\/th>Titre<\/strong><\/th>Equipe Inria<\/strong><\/th>Financement<\/strong><\/th>Dur\u00e9e<\/strong><\/th>Type<\/strong><\/th><\/tr><\/thead>
2020<\/strong><\/td><\/td><\/td><\/td><\/td><\/td><\/tr>
1<\/td>Couplage d\u2019un mod\u00e8le num\u00e9rique d\u2019\u00e9olienne avec un algorithme de type \u00ab Operational Modal Analysis \u00bb (OMA)<\/td>I4S<\/td>IFPEN<\/td>2020\u20132023<\/td>Ph.D.<\/td><\/tr>
2<\/td>Combined Machine Learning and DFT simulations to accelerate the identification of catalytic reaction mechanisms<\/td>MATHERIALS<\/td>Inria<\/td>2020\u20132023<\/td>Ph.D.<\/td><\/tr>
3<\/td>Inversion robuste d\u2019un code de calcul prenant en entr\u00e9es des donn\u00e9es de nature fonctionnelle. Application \u00e0 la conception d\u2019\u00e9oliennes<\/td>AIRSEA<\/td>Inria<\/td>2020\u20132023<\/td>Ph.D.<\/td><\/tr>
4<\/td>Construction automatique d\u2019un mod\u00e8le dynamique de suivi d\u2019interface par analyse d\u2019images 4D<\/td>FLUMINANCE<\/td>IFPEN<\/td>2020\u20132021<\/td>post-doc<\/td><\/tr>
<\/td><\/td><\/td><\/td><\/td><\/td><\/tr>
<\/td><\/td><\/td><\/td><\/td><\/td><\/tr>
2021<\/strong><\/td><\/td><\/td><\/td><\/td><\/td><\/tr>
5<\/td>Mod\u00e9lisation de l\u2019a\u00e9ro\u00e9lasticit\u00e9 en grandes transformations par un couplage partitionn\u00e9 : application aux grandes \u00e9oliennes<\/td>MEMPHIS<\/td>IFPEN<\/td>2021\u20132024<\/td>Ph.D.<\/td><\/tr>
6<\/td>D\u00e9finition de conditions aux limites \u00e0 la p\u00e9riph\u00e9rie d’une zone de stockage de CO2\/Hydrog\u00e8ne au moyen de bases r\u00e9duites<\/td>MATHERIALS<\/td>IFPEN<\/td>2021\u20132024<\/td>Ph.D.<\/td><\/tr>
7<\/td>D\u00e9veloppement de m\u00e9thodes d\u2019ordre \u00e9lev\u00e9 dans un code cart\u00e9sien AMR\u2013CutCell pour la mod\u00e9lisation LES de la combustion<\/td>CAGIRE<\/td>IFPEN<\/td>2021\u20132024<\/td>Ph.D.<\/td><\/tr>
8<\/td>Apprentissage par renforcement profond avec contraintes et d\u00e9monstrations<\/td>DYOGENE<\/td>Inria<\/td>2021\u20132024<\/td>Ph.D.<\/td><\/tr>
9<\/td>M\u00e9thodes num\u00e9riques avanc\u00e9es pour les probl\u00e8mes \u00e0 forte raideur en transport r\u00e9actif<\/td>RAPSODI<\/td>Inria<\/td>2021\u20132024<\/td>Ph.D.<\/td><\/tr>
10<\/td>Acceleration of wind farm flow simulations by means of data-driven machine learning techniques<\/td>MEMPHIS<\/td>Inria<\/td>2021\u20132023<\/td>Ph.D.<\/td><\/tr>
<\/td><\/td><\/td><\/td><\/td><\/td><\/tr>
<\/td><\/td><\/td><\/td><\/td><\/td><\/tr>
2022<\/strong><\/td><\/td><\/td><\/td><\/td><\/td><\/tr>
11<\/td>Vectorisation portable avec ma\u00eetrise de la pr\u00e9cision num\u00e9rique pour des codes de simulation multi-pr\u00e9cision<\/td>STORM<\/td>IFPEN<\/td>2022\u20132025<\/td>Ph.D.<\/td><\/tr>
12<\/td>Discr\u00e9tisation sur maillages non co\u00efncidents de mod\u00e8les poro-m\u00e9caniques avec prise en compte du contact frictionnel au niveau des failles<\/td>COFFEE<\/td>Inria<\/td>2022\u20132023<\/td>post-doc<\/td><\/tr>
13<\/td>Optimisation pseudo temps-r\u00e9el des performances environnementales de la mobilit\u00e9 urbaine gr\u00e2ce \u00e0 des approches de mod\u00e9lisation macroscopique et multi-modale<\/td>ACUMES<\/td>IFPEN<\/td>2022\u20132025<\/td>Ph.D.<\/td><\/tr>
14<\/td>Optimisation de topologie de graphes routiers bas\u00e9e sur des donn\u00e9es et application \u00e0 l’urbanisme tactique et durable dans de grandes m\u00e9tropoles<\/td>DANCE<\/td>Inria<\/td>2022\u20132025<\/td>Ph.D.<\/td><\/tr>
<\/td><\/td><\/td><\/td><\/td><\/td><\/tr>
<\/td><\/td><\/td><\/td><\/td><\/td><\/tr>
2023<\/strong><\/td><\/td><\/td><\/td><\/td><\/td><\/tr>
15<\/td>Evaluation des constantes de r\u00e9actions de d\u00e9shydratation d’alcools sur alumine par approche coupl\u00e9e machine learning-chimie quantique<\/td>MATHERIALS<\/td>IFPEN<\/td>2023\u20132026<\/td>Ph.D.<\/td><\/tr>
16<\/td>M\u00e9thodes de pr\u00e9conditionnement non lin\u00e9aire avanc\u00e9es pour la r\u00e9solution des probl\u00e8mes \u00e0 fortes h\u00e9t\u00e9rog\u00e9n\u00e9it\u00e9s en g\u00e9osciences<\/td>COFFEE<\/td>IFPEN<\/td>2023\u20132026<\/td>Ph.D.<\/td><\/tr>
17<\/td>Development of data-driven approaches for physics-informed wind-turbine digital twins and application to real-world data<\/td>I4S<\/td>IFPEN<\/td>2023\u20132026<\/td>Ph.D.<\/td><\/tr>
18<\/td>Screen2Learn – Exploiter la biodiversit\u00e9 : une approche de criblage et d’apprentissage<\/td>InBio<\/td>Inria<\/td>2023\u20132026<\/td>Ph.D.<\/td><\/tr>
19<\/td>Pilotage des m\u00e9thodes adaptatives (m\u00e9thodes AMR, multi-niveaux) \u00e0 l\u2019aide de m\u00e9thodes de machine learning de type GNN<\/td>Tau<\/td>Inria<\/td>2023\u20132026<\/td>Ph.D.<\/td><\/tr>
20<\/td>Time dependent reliability based design optimization with nonlinear numerical models. Application to the design of an offshore wind turbine<\/td>ASCII<\/td>Inria<\/td>2023\u20132026<\/td>Ph.D.<\/td><\/tr>
<\/td><\/td><\/td><\/td><\/td><\/td><\/tr>
<\/td><\/td><\/td><\/td><\/td><\/td><\/tr>
2024<\/strong><\/td><\/td><\/td><\/td><\/td><\/td><\/tr>
21<\/td>Conceptualisation et design d\u2019un framework d\u2019inf\u00e9rence de DNNs d\u00e9di\u00e9 \u00e0 la simulation massivement parall\u00e8le sur des architectures Exascale<\/td>DATAMOVE<\/td>Inria<\/td>2024\u20132027<\/td>Ph.D.<\/td><\/tr>
22<\/td>Analyse math\u00e9matique et r\u00e9solution num\u00e9rique de quelques syst\u00e8mes diff\u00e9rentiels raides en mod\u00e9lisation biog\u00e9ochimique<\/td>PARADYSE<\/td>IFPEN<\/td>2024\u20132027<\/td>Ph.D.<\/td><\/tr>
23<\/td>Apprentissage actif pour des entr\u00e9es fonctionnelles : application \u00e0 l\u2019optimisation et \u00e0 l\u2019estimation d\u2019ensembles admissibles<\/td>AIRSEA<\/td>IFPEN<\/td>2024\u20132027<\/td>Ph.D.<\/td><\/tr>
24<\/td>Contr\u00f4le \u00e0 champ moyen pour la gestion de flexibilit\u00e9s diffuse<\/td>DISCO<\/td>IFPEN<\/td>2024\u20132027<\/td>Ph.D.<\/td><\/tr>
25<\/td>Decentralized learning and its industrial applications<\/td>DYOGENE<\/td>IFPEN<\/td>2024\u20132027<\/td>Ph.D.<\/td><\/tr>
26<\/td>Adaptitivt\u00e9 et estimateur d’erreur a posteriori pour le stockage du CO2<\/td>SERENA<\/td>Inria<\/td>2025<\/td>post-doc<\/td><\/tr>
27<\/td>Algorithmes de couplage it\u00e9ratif et de r\u00e9solution des syst\u00e8mes non-lin\u00e9aires pour la simulation de mod\u00e8les Thermo-Hydro-M\u00e9caniques faill\u00e9s<\/td>GALETS<\/td>Inria<\/td>2025<\/td>post-doc<\/td><\/tr>
<\/td><\/td><\/td><\/td><\/td><\/td><\/tr>
<\/td><\/td><\/td><\/td><\/td><\/td><\/tr>
2025<\/strong><\/td><\/td><\/td><\/td><\/td><\/td><\/tr>
28<\/td>Applications de techniques de r\u00e9duction de mod\u00e8le aux simulations Thermo-Hydro-M\u00e9caniques en milieu poreux<\/td>MATHERIALS<\/td>Inria<\/td>2025\u20132028<\/td>Ph.D.<\/td><\/tr>
29<\/td>Evaluation des approches hybrides physique \/ machine learning pour la mod\u00e9lisation de la colonne d’absorption d’une unit\u00e9 de captage de CO2<\/td>MALICE<\/td>IFPEN<\/td>2025\u20132028<\/td>Ph.D.<\/td><\/tr>
30<\/td>Mod\u00e9lisation de biofilms souterrains pour am\u00e9liorer la s\u00e9questration de carbone<\/td>PLEIADE<\/td>Inria<\/td>2026<\/td>post-doc<\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"

No Titre Equipe Inria Financement Dur\u00e9e Type 2020 1 Couplage d\u2019un mod\u00e8le num\u00e9rique d\u2019\u00e9olienne avec un algorithme de type \u00ab Operational Modal Analysis \u00bb (OMA) I4S IFPEN 2020\u20132023 Ph.D. 2 Combined Machine Learning and DFT simulations to accelerate the identification of catalytic reaction mechanisms MATHERIALS Inria 2020\u20132023 Ph.D. 3 Inversion\u2026<\/p>\n

Continue reading<\/span><\/i><\/a> <\/p>\n","protected":false},"author":932,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-239","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/ifpen\/wp-json\/wp\/v2\/pages\/239","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/ifpen\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/ifpen\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/ifpen\/wp-json\/wp\/v2\/users\/932"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/ifpen\/wp-json\/wp\/v2\/comments?post=239"}],"version-history":[{"count":51,"href":"https:\/\/project.inria.fr\/ifpen\/wp-json\/wp\/v2\/pages\/239\/revisions"}],"predecessor-version":[{"id":462,"href":"https:\/\/project.inria.fr\/ifpen\/wp-json\/wp\/v2\/pages\/239\/revisions\/462"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/ifpen\/wp-json\/wp\/v2\/media?parent=239"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}