Research

 

 

Research topics:

  1. HPF: adaptive hierarchical sub-tensor approximations using local HOSVD (Hierarchical Partitioning Format)
  2. Non-linear model reduction based on Riemannian geometry
  3. CP-TT: using TT-SVD to greedily construct stable CP tensor approximations.
  4. SO-TT: greedy approximation of a given tensor as a sum of Tensor Trains.
  5. An extended Krylov-like method to solve multilinear systems.
  6. State estimation using Tensor Trains.

 

 

Conferences and Seminars:

  1. Virginie Ehrlacher,  Dynamical Adaptive tensor methods for the Vlasov-Poisson system, GAMN 2019, Wien. GAMN2019
  2. Damiano Lombardi, Adaptive dynamical approximations with tensors, Amiens 2019. Amiens2019
  3. Damiano Lombardi, A hierarchical adaptive subtensor partitioning for tensor compression, Lions-Magenes days, Paris 2019.
  4. Damiano Lombardi, A hierarchical adaptive subtensor partitioning for tensor compression, Mortech, Paris 2019.
  5. Damiano Lombardi, Adaptive tensor methods for scientific computing, Séminaire du Laboratoire Jacques-Louis Lions, Paris, 05-03-2021
  6. Damiano Lombardi, Adaptive tensor methods for scientific computing, Seminar of  Glasgow Centre of Computational Engineering,  April-2021

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