cv

Basics

Name Lucas Lestandi
Label Associate Professor
Email llestand@ec-nantes.fr
Phone +33 2 40 37 15 17
Url https://llestandi.github.io
Summary Hi, I'm Lucas, associate professor (MCF) at Ecole Centrale Nantes, France. I mostly focus on the following aspects. Reduced Order Modeling (intrusive), Surrogate modeling (non-intrusive), Machine Learning for science, POD/SVD. Dimension reduction, tensor decomposition. With various application fields e.g. Complex flows, Additive manufacturing of metals, etc.

Work

  • 2022 - Present
    Associate professor in computational mechanics
    Ecole Centrale de Nantes, Laboratoire GeM
    As a member of the Maths and Computer Science department, I teach mostly computer science courses (programming, python, data visualization...). I run my research at GeM laboratory in the MECNUM team. I focus on data driven approaches to solve faster complex mechanics problem which involves large models and data sets.
    • Additive manufacturing of metals
    • Large dimension reduction techniques
  • 2020.05 - 2021.12
    Scientist
    Institute of High Performance Computing, A*Star
    Surrogate modeling for Additive Manufacturing Marine & offshore structures
    • Data driven models
    • Physics informed NN
    • ROM
    • geometric parametrization
  • 2019.04 - 2020.03
    Research Fellow
    SPMS, Nanyang Technological University, Singapore
    Investigating neural networks for PDEs Tutorials in mathematics for engineering.
  • 2015.10 - 2018.10
    Teacher Assistant
    Université de Bordeaux, Bordeaux INP
    Practical work (TP) at IUT Mesure physique Travaux Dirigés Fluid Dynamics, MATMECA
  • 2015.02 - 2015.07
    Research Intern
    INRIA
    3D implementation of fluid dynamics code to compute trajectories of ice chunks formed on aircrafts. level-set, vortex-in-cell, IBM, etc.

Awards

Education

  • 2015 - 2018

    Bordeaux, France

    PhD in mechanics
    I2M/TREFLE, Université de Bordeaux
    Low rank approximation techniques and reduced order modeling applied to some fluid dynamics problems.
    • Tensor decomposition
    • POD analysis of bifurcation sequence in LDC flow
    • ROM (a) "physical" interpolation, (b) POD Galerkin
  • 2014 - 2015

    Bordeaux, France

    MSc
    Université de Bordeaux
    Applied Mathematics
  • 2012 - 2015

    Bordeaux, France

    Diplome d'ingénieur - Masters degree in Engineering
    ENSEIRB-MATMECA, Bordeaux
    Mathematical modelling and mechanics

Publications

Skills

Computational Physics
Computational fluid dynamics
finite differences
finite elements
additive manufacturing
Surrogate modelling
Reduced order modelling
data-driven approaches
Machine learning
POD
ROM
Tensor decomposition
programming
Python
numpy
latex
md/pandoc
VTK
C++
parallel programming
bash
linux

Languages

French
Native speaker
English
Fluent

Interests

Research
surrogate modeling
data driven models
deep learning for PDEs
data reduction
tensor decomposition
PINN
reduced order modeling
POD
tensor trains
projection ROM
interpolation ROM
additive manufacturing
complex flow simulation
bifurcations
instabilities