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PGML - Research project
Physics-Guided Machine Learning
ABSTRACT
The aim of the PGML project was to combine the strengths of physics and machine learning to improve the modelling of physical phenomena. Currently, deep learning requires large quantities of data to create accurate models, while physics uses rigorous theories with few data. The idea was therefore to integrate theoretical knowledge of physics into machine learning algorithms to create more efficient models with less data. The project has led to a fruitful collaboration between computer science and physics, with each field benefiting from the advances of the other. In particular, it has made it possible to model the functionalisation of surfaces, under certain conditions, using femto-second lasers.
PUBLICATIONS
- Eduardo Brandao, Anthony Nakhoul, Stefan Duffner, R. Emonet, Florence Garrelie, Amaury Habrard, François Jacquenet, Florent Pigeon, Marc Sebban, and Jean-Philippe Colombier, "Learning Complexity to Guide Light-Induced Self-Organized Nanopatterns",Phys. Rev. Lett. 130, 226201, May 2023
https://doi.org/10.1103/PhysRevLett.130.226201 - Eduardo Brandao, Jean-Philippe Colombier, Stefan Duffner, Rémi Emonet, Florence Garrelie, Amaury Habrard, François Jacquenet, Anthony Nakhoul, and Marc Sebban. 2022. "Learning PDE to Model Self-Organization of Matter" Entropy 24, no. 8: 1096.
https://doi.org/10.3390/e24081096 - Eduardo Brandao, Stefan Duffner, Rémi Emonet, Amaury Habrard, François Jacquenet & Marc Sebban , "Is My Neural Net Driven by the {MDL} Principle?", Proceedings of the European Conférence on Machine Learning and Knowledge Discovery in Databases
https://doi.org/10.1007/978-3-031-43415-0_11
CONFERENCES
- Eduardo Brandao, Stefan Duffner, Rémi Emonet, Amaury Habrard, François Jacquenet & Marc Sebban, "Is My Neural Net Driven by the {MDL} Principle?", European Conference on Machine Learning and Knowledge Discovery in Databases
About the PGML project - Thesis certified
RESEARCH AXIS
Axis #2
KEYWORDS
Machine Learning, Physical phenomena modeling, background knowledge
DURATION - STATUS
01/10/2020 – 30/09/2023 - Completed
PhD STUDENT
Eduardo BRANDAO (LabHC)
PROJECT COORDINATORS
François JACQUENET (LabHC)
Rémi EMONET (LabHC)
COORDINATING LABORATORY
Hubert Curien Laboratory (LabHC)
PARTNER LABORATORIES
LIRIS Laboratory (LIRIS)
PARTNER RESEARCHERS
Jean-Philippe COLOMBIER (LabHC)
Stefan DUFFNER (LIRIS)
Florence GARRELIE (LabHC)
Amaury HABRARD (LabHC)
Anthony NAKHOUL (LabHC)
Marc SEBBAN (LabHC)
Axis #2
KEYWORDS
Machine Learning, Physical phenomena modeling, background knowledge
DURATION - STATUS
01/10/2020 – 30/09/2023 - Completed
PhD STUDENT
Eduardo BRANDAO (LabHC)
PROJECT COORDINATORS
François JACQUENET (LabHC)
Rémi EMONET (LabHC)
COORDINATING LABORATORY
Hubert Curien Laboratory (LabHC)
PARTNER LABORATORIES
LIRIS Laboratory (LIRIS)
PARTNER RESEARCHERS
Jean-Philippe COLOMBIER (LabHC)
Stefan DUFFNER (LIRIS)
Florence GARRELIE (LabHC)
Amaury HABRARD (LabHC)
Anthony NAKHOUL (LabHC)
Marc SEBBAN (LabHC)