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PIMALEA - Research project
Physics-Informed MAchine LEArning: From Extraction to Transfer of Knowledge in Surface Engineering
Post-doctoral fellow: Eduardo BRANDAO
ABSTRACT
The goal of the PIMALEA research project is to combine the interdisciplinary skills present at the Hubert Curien lab in statistical learning and laser-matter interaction to foster the development of new joint methodological contributions in intelligent self-organization of matter. This project requires strong skills in applied mathematics, statistics, optimization and laser-matter interaction present in two complementary teams of the LabHC (“Data Intelligence” and “Laser-Matter Interaction”).
PIMALEA has been thought and designed to be a win-win project: on the one hand, laser-matter interaction raises numerous machine learning challenges, including (i) a limited access to training data and the availability of only partial and incomplete physical knowledge and (ii) a strong necessity to transfer knowledge from one learned dynamical system to another. On the other hand, the advances that will be carried out in machine learning will allow to better understand the physics underlying the mechanisms of laser/radiation-matter interaction, enabling to address numerous societal challenges in the fields of space, nuclear, defense, energy or health.
PIMALEA has been thought and designed to be a win-win project: on the one hand, laser-matter interaction raises numerous machine learning challenges, including (i) a limited access to training data and the availability of only partial and incomplete physical knowledge and (ii) a strong necessity to transfer knowledge from one learned dynamical system to another. On the other hand, the advances that will be carried out in machine learning will allow to better understand the physics underlying the mechanisms of laser/radiation-matter interaction, enabling to address numerous societal challenges in the fields of space, nuclear, defense, energy or health.
ABOUT the PIMALEA project
RESEARCH AXES
Axis #2
Axis #1
KEYWORDS
Physics-informed Machine Learning,
Self-organization of matter, PDE learning.
DURATION - STATUS
01/12/2023 – 31/05/2025 - Ongoing
POST-DOCTORAL FELLOW
Eduardo BRANDAO (LabHC)
PROJECT COORDINATOR
Rémi EMONET (LabHC)
COORDINATING LABORATORY
Hubert Curien Laboratory (LabHC)
PARTNER RESEARCHERS
Jean-Philippe COLOMBIER (LabHC)
Axis #2
Axis #1
KEYWORDS
Physics-informed Machine Learning,
Self-organization of matter, PDE learning.
DURATION - STATUS
01/12/2023 – 31/05/2025 - Ongoing
POST-DOCTORAL FELLOW
Eduardo BRANDAO (LabHC)
PROJECT COORDINATOR
Rémi EMONET (LabHC)
COORDINATING LABORATORY
Hubert Curien Laboratory (LabHC)
PARTNER RESEARCHERS
Jean-Philippe COLOMBIER (LabHC)