MALICE

MAchine Learning with Integration of surfaCe Engineering knowledge

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Lab. Hubert Curien

UMR CNRS 5516

Saint-Etienne, FRANCE

The goal of the MALICE Inria project-team is to combine the interdisciplinary skills present at the Hubert Curien laboratory in statistical learning and laser-matter interaction to foster the development of new joint methodological contributions at the interface between Machine Learning and Surface Engineering.

Axis 1: Theoretical frameworks when learning from data and background knowledge
Axis 2: Integration and extraction of knowledge in surface engineering
Axis 3: Domain generalization and transfer learning for surface engineering

As such, MALICE is inherently rooted at the crossroads of Applied Mathematics, Statistical Learning Theory, Optimization, Physics and Differentiable Simulation.

news

Dec 1, 2023 Creation of the Inria project team.

selected publications

  1. 2022_Brandao_E_j_-entropy_lpmsom.png
    Learning PDE to model self-organization of matter
    Eduardo Brandao, Jean-Philippe Colombier, Stefan Duffner, and 6 more authors
    Entropy, 2023
Inria UJM LabHC CNRS