Teaching activities

Teaching undergraduate students at École Polytechnique

• 2020 - 2023 Physics I: Mechanics and heat
  • Tutorials sessions
  • Designing and writing tutorials subjects
• 2020 - 2023 Physics Lab I
  • Lab sessions
  • Creation of lab session subjects

Physique-chimie PSI-PSI* Concours X, ENS, CentraleSupélec, Mines-Ponts, CCINP

• Writing a physics book for undergraduate students (Classes Préparatoires aux Grandes Écoles)
• Physics problems, corrections, and comments, helping students prepare for their entrance exams to prestigious universities (French “Grandes Écoles”).

Advanced-level courses

I designed notebooks about dimensionnality reduction in both linear and non-linear contexts. Those few short courses relevant to model reduction are available on my github

• Course 1 Non-linear manifold learning: SVD and kernel PCA
• Course 2 Non-linear manifold learning: Autoencoders
• Course 3 NN-FEM, simplified implementation of NeuROM (Daby-Seesaram, Škardová, and Genet 2024) in 1D to get started with solving PDEs in the HiDeNN framweork (Zhang et al. 2021)

References

Daby-Seesaram, Alexandre, Kateřina Škardová, and Martin Genet. 2024. “Neurom.” Zenodo. https://doi.org/10.5281/zenodo.13907063.

Zhang, Lei, Lin Cheng, Hengyang Li, Jiaying Gao, Cheng Yu, Reno Domel, Yang Yang, Shaoqiang Tang, and Wing Kam Liu. 2021. “Hierarchical Deep-Learning Neural Networks: Finite Elements and Beyond.” Computational Mechanics 67 (1): 207–30. https://doi.org/10.1007/s00466-020-01928-9.