Additional resources
A 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)
PhD dissertation
My PhD dissertation is available here.
PhD dissertation template
In collaboration with Flavien Loiseau, we made our Ph.D. thesis template openly available on Github.
CV
A more detailed version of my background can be found in my CV.
NeuROM (Daby-Seesaram, Škardová, and Genet 2024)
The reduced-order modelling code used to create surrogate models based on an hybridisation of standard reduced-order modelling methods such as the PGD and Deep learning methods is public and notebooks with tutorials are also available.
Illustrations
Hybrid sparse neural network and Proper Generalised Decomposition (PGD)
Parametric interactive results
Feel free to interact with it.
3D lung registration interactive results
Feel free to interact with it.
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.