Kellian Cottart

Kellian Cottart

Doctoral student in Bayesian AI and Neuromorphic engineering at Université Paris-Saclay | Data Sciences at Ecole Polytechnique (IPP2023) | Engineer's degree at Ecole Centrale d'Electronique

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publications

A unified method to generate representative volume elements with tailored random fibre arrangements to estimate the shear and transverse behaviours of unidirectional continuous fibres composite plies

Published in Journal Of Composite Materials, 2024

Different microstructures with or without clusters of fibres (resulting from manufacturing processes) are necessary to quantify the mechanical variability. The variability of the microstructures is characterised by the variation of the arrangement, the volume fraction and the misalignment of the fibres.

Recommended citation: Mechin P-Y, Borras A, Cottart K, Keryvin V. A unified method to generate representative volume elements with tailored random fibre arrangements to estimate the shear and transverse behaviours of unidirectional continuous fibres composite plies. Journal of Composite Materials. 2024;0(0). doi: 10.1177/00219983241300144
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Dynamic Control of Weight-Update Linearity in Magneto-Ionic Synapses

Published in Nanoletters, 2025

We demonstrate that magneto-ionic devices can perform as synaptic elements with dynamically tunable depression linearity controlled by an external magnetic field, a functionality reminiscent of neuromodulation in biological systems.

Recommended citation: Bernard, Guillaume, et al. "Dynamic control of weight-update linearity in magneto-ionic synapses." Nano Letters 25.4 (2025): 1443-1450. doi: 10.1021/acs.nanolett.4c05247
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Bayesian continual learning and forgetting in neural networks

Published in Nature Communications, 2025

We introduce Metaplasticity from Synaptic Uncertainty (MESU), a Bayesian update rule that scales each parameter’s learning by its uncertainty, enabling a principled combination of learning and forgetting without explicit task boundaries.

Recommended citation: Bonnet, Djohan, et al. "Bayesian continual learning and forgetting in neural networks." Nature Communications 16.1 (2025): 9614.
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talks

Neuromorphic Symposium: Bayesian Continual Learning and Forgetting in Neural Networks (15 minutes)

2 minute read

Published:

When neural networks are deployed in the real world, data does not stay stationary. Environments evolve, tasks change, and distributions drift. Standard neural networks suffer from catastrophic forgetting: when learning something new, they overwrite what they learned before. But there is a second, often ignored challenge: catastrophic remembering, when networks become too rigid, stop adapting, and lose their ability to fit new data, leading to overconfident predictions.

teaching