{"id":96,"date":"2019-01-29T08:01:33","date_gmt":"2019-01-29T07:01:33","guid":{"rendered":"https:\/\/project.inria.fr\/adapt\/?page_id=96"},"modified":"2021-10-13T18:00:01","modified_gmt":"2021-10-13T16:00:01","slug":"research","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/adapt\/research\/","title":{"rendered":"Research"},"content":{"rendered":"

\"\"<\/a><\/p>\n

 <\/p>\n

 <\/p>\n

Research topics:<\/p>\n

    \n
  1. HPF: adaptive hierarchical sub-tensor approximations using local HOSVD<\/a> (H<\/strong>ierarchical P<\/strong>artitioning F<\/strong>ormat)<\/li>\n
  2. Non-linear model reduction based on Riemannian geometry<\/a><\/li>\n
  3. CP-TT: using TT-SVD to greedily construct stable CP tensor approximations<\/a>.<\/li>\n
  4. SO-TT: greedy approximation of a given tensor as a sum of Tensor Trains.<\/a><\/li>\n
  5. An extended Krylov-like method to solve multilinear systems.<\/a><\/li>\n
  6. State estimation using Tensor Trains.<\/a><\/li>\n<\/ol>\n

     <\/p>\n

     <\/p>\n

    Conferences and Seminars:<\/p>\n

      \n
    1. Virginie Ehrlacher, \u00a0Dynamical Adaptive tensor methods for the Vlasov-Poisson system<\/em>, GAMN 2019, Wien.\u00a0GAMN2019<\/a><\/li>\n
    2. Damiano Lombardi, Adaptive dynamical approximations with tensors<\/em>, Amiens 2019.\u00a0Amiens2019<\/a><\/li>\n
    3. Damiano Lombardi, A hierarchical adaptive subtensor partitioning for tensor compression<\/i>, Lions-Magenes days,\u00a0Paris 2019<\/a>.<\/li>\n
    4. Damiano Lombardi, A hierarchical adaptive subtensor partitioning for tensor compression<\/i>, Mortech,\u00a0Paris 2019<\/a>.<\/li>\n
    5. Damiano Lombardi, Adaptive tensor methods for scientific computing<\/em>, S\u00e9minaire du Laboratoire Jacques-Louis Lions, Paris, 05-03-2021<\/li>\n
    6. Damiano Lombardi, Adaptive tensor methods for scientific computing<\/em>, Seminar of \u00a0Glasgow Centre of Computational Engineering, \u00a0April-2021<\/li>\n<\/ol>\n

      <\/p>","protected":false},"excerpt":{"rendered":"

          Research topics: HPF: adaptive hierarchical sub-tensor approximations using local HOSVD (Hierarchical Partitioning Format) Non-linear model reduction based on Riemannian geometry CP-TT: using TT-SVD to greedily construct stable CP tensor approximations. SO-TT: greedy approximation of a given tensor as a sum of Tensor Trains. An extended Krylov-like method to\u2026<\/p>\n

      Continue reading<\/span><\/i><\/a> <\/p>\n","protected":false},"author":1500,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-96","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/adapt\/wp-json\/wp\/v2\/pages\/96","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/adapt\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/adapt\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/adapt\/wp-json\/wp\/v2\/users\/1500"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/adapt\/wp-json\/wp\/v2\/comments?post=96"}],"version-history":[{"count":18,"href":"https:\/\/project.inria.fr\/adapt\/wp-json\/wp\/v2\/pages\/96\/revisions"}],"predecessor-version":[{"id":150,"href":"https:\/\/project.inria.fr\/adapt\/wp-json\/wp\/v2\/pages\/96\/revisions\/150"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/adapt\/wp-json\/wp\/v2\/media?parent=96"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}