Machine learning principles and applications in physics - Département de Physique de l'Ecole Normale supérieure

Why choose this program? Program, courses and calendar Master ICFP 1st year (2023-2024) 2023-2024 Calendar and timetable Courses Internships Program, courses and calendar Master ICFP 1st year (2022-2023) 2022-2023 Calendar and timetable Courses Internships Program, courses and calendar Master ICFP 2nd year 2023-2024 First Semester Second Semester "Physics Now" Seminars 2019/2020 "Physics Now" Seminars 2020/2021 "Physics Now" Seminars 2021/2022 "Physics Now" Seminars 2023/2024 "Physics Now" Seminars 2022/2023 Internships 2023 2024 calendar Program, courses and calendar Master ICFP 2nd year 2022-2023 First Semester Second Semester "Physics Now" Seminars 2019/2020 "Physics Now" Seminars 2020/2021 "Physics Now" Seminars 2021/2022 "Physics Now" Seminars 2022/2023 Internships 2022 2023 calendar Admission and Language Requirements How to apply Information for students Information for International Students Fees and Scholarships Careers Master Intranet Archives Erreur ( A supprimer) Program, courses and calendar Master ICFP 1st year (2021-2022) Program, courses and calendar Master ICFP 1st year (2021-2022) Program, courses and calendar Master ICFP 2nd year 2021-2022 Program, courses and calendar Master ICFP 2nd year 2020-2021 Program, courses and calendar Master ICFP 2nd year 2019-2020 Archives 2015-2016 Archives 2016-2017 Archives 2017-2018 Archives 2018-2019 Archives 2019-2020 Archives 2020-2021

Machine learning principles and applications in physics

Quick links

Next student seminar :
Access to the program

Here you can find information about your internships:
Experimental Internship - Undergraduate program
Master ICFP first year Internship

News : ICFP Research seminars
November 14 - 18, 2022 :
All information about the program

Contact us - Student support and Graduate School office :
Tél : 01 44 32 35 60
enseignement@phys.ens.fr

Quick links

Next student seminar :
Access to the program

Here you can find information about your internships:
Experimental Internship - Undergraduate program
Master ICFP first year Internship

News : ICFP Research seminars
November 14 - 18, 2022 :
All information about the program

Contact us - Student support and Graduate School office :
Tél : 01 44 32 35 60
enseignement@phys.ens.fr

Lectures and projects: Tony Bonnaire and Jorge Fernandez de Cossio Diaz
ECTS credits: 6
Language of instruction : English
Keywords: Neural networks; Supervised learning; Generative models; Deep learning; Sampling; Applications in physics.
Exam: Oral presentation

Prerequisites

Python basis for numerical analysis (ML-specific libraries are not required), knowledge in statistics (estimation, inference).

Description

In the first part (3 lectures), we will expose the bases of machine learning (supervised/unsupervised ML, optimization, regularization, neural networks, deep neural networks etc.) as well as the Python tools required to initiate a project (PyTorch).

After this short introduction we will present a set of projects (some are listed below) on which the students can work in groups of two during the whole semester. All the topics are linked with machine learning applied to a physics problem.
After some bibliographical work, students will have to find appropriate datasets and pre-process them to build and implement an algorithm that answers the problem. The work is done in partial autonomy with weekly interactions planned for each group with the supervisors.

Final examination

The exam consists of a 20-minute oral presentation of the proposed solution but also of the scientific approach followed throughout the project.

Projects

A non-exhaustive (and not definitive) list:
Estimating cosmological parameters from halo distribution in cosmological simulations,
Augmenting dark matter simulations’ resolution by deep generative models,
Deep symbolic regression for new physics,
Contrastive divergence learning in energy-based models,
Applications of autoregressive networks in statistical mechanics problems,
Accelerated sampling of energy landscapes with neural networks.

Bibliography

Carleo, Giuseppe, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, and Lenka Zdeborová. "Machine learning and the physical sciences." Reviews of Modern Physics 91, no. 4 (2019): 045002.
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016. Freely available here: https://www.deeplearningbook.org/
PyTorch: https://pytorch.org/.

Quick links

Next student seminar :
Access to the program

Here you can find information about your internships:
Experimental Internship - Undergraduate program
Master ICFP first year Internship

News : ICFP Research seminars
November 14 - 18, 2022 :
All information about the program

Contact us - Student support and Graduate School office :
Tél : 01 44 32 35 60
enseignement@phys.ens.fr