Welcome to my webpage !

Here you will find all my research material

About me

I am Eloi Martinet, a postdoctoral researcher at the University of Würzburg and part of the team of Mathematics of Machine Learning. I am interested in using a variational approach to tackle machine learning problems, and using neural networks capabilities to solve shape optimization problems.

In October 2023, I defended my PhD Thesis about spectral shape optimization supervised by Dorin BUCUR and Edouard OUDET. You can download the manuscript here.

You can download my CV here.

Education

  • Postdoc in Variational Methods in Machine Learning.
    JMU (Würzburg, Germany)
  • PhD in Spectral Shape Optimization, 2019-2023
    LAMA (Chambéry), LJK (Grenoble)
  • Agrégation de Mathématiques, 2020-2021
    Institut Fourier (Grenoble)
  • Engineering School in Informatics and Applied Mathematics, 2016-2019
    ENSIMAG (Grenoble)

Research interests

  • Shape and topology optimization
  • Numerical methods
  • Level set method
  • Machine Learning

Teaching

  • 2024-2025 : Lectures and exercise session of Mathematical Foundations of Data Science, JMU (Würzburg)
  • 2024-2025 : Seminar of Introduction to Data Science, JMU (Würzburg)
  • 2024 : Seminar of Machine Learning on Graphs, JMU (Würzburg)
  • 2024 : Working Group on FEM and the use of Neural Networks as PDE solver, JMU (Würzburg)
  • 2022 - 2023 : tutoring of practical sessions of numerical analysis, ENSIMAG (Grenoble)
  • 2022 - 2023 : tutoring of practical sessions of integration and Fourier transform, ENSIMAG (Grenoble)
  • 2021 - 2022 : course + tutoring of practical sessions of basic analysis, UGA (Grenoble)
  • 2019 - 2020 : tutoring of practical sessions of numerical analysis, USMB (Chambery)

Supervision

  • 2024 : Co-supervison of the MSc Internship of Nicolas Roblet with Romain Joly titled Spectrum of unbounded operators and applications to PDEs (report).

Publications

& preprints


Teaching ressources

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Numerical Methods for PDEs
From the Finite Element Method to Neural Networks

Exploring old and recent ways to solve PDEs. Given at the University of Würzburg in the summer semester of 2024.

Go to the course