Alexandre Daby-Seesaram

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Welcome,

I’m Alexandre Daby-Seesaram, an Assistant Professor at ENSTA within the Institut Polytechnique de Paris.

I hold a Ph.D. in computational mechanics from the University Paris-Saclay (ENS Paris-Saclay), where I worked at the LMPS (Laboratory of Mechanics Paris-Saclay). After my PhD, I moved to École polytechnique for a two-year postdoc where I started applying reduced-order modelling methods to bio-mechanics to build patient-specific digital twins.

My main research interest consists of solving highly non-linear problems using model-order reduction techniques such as the Proper Generalised Decomposition (PGD). I’ve recently become interested in hybridising classical model-order reduction methods with machine learning approaches. Combining tensor decomposition and interpretable neural networks enables efficient and robust solving of large non-linear problems.

The ultimate goal of my research is to achieve individualisation and real-time simulation of human organs digital twins using model-order reduction techniques, thus going from theoretical tools to clinical applications.

Augmented reality interactive lung digital twin based on FENNI-PGD (Daby-Seesaram et al. 2025)

Thanks to (Mathur et al. 2023) for the details on how to set up the AR environment

References

Daby-Seesaram, Alexandre, Kateřina Škardová, and Martin Genet. 2025. “Finite Element Neural Network Interpolation: Part II—Hybridisation with the Proper Generalised Decomposition for Non-Linear Surrogate Modelling.” Computational Mechanics, ahead of print, August 30. https://doi.org/10.1007/s00466-025-02676-4.
Mathur, Mrudang, Josef M. Brozovich, and Manuel K. Rausch. 2023. “A Brief Note on Building Augmented Reality Models for Scientific Visualization.” Finite Elements in Analysis and Design 213 (January): 103851. https://doi.org/10.1016/j.finel.2022.103851.