Perceval Beja-Battais
About me
I’m a 3rd year PhD Student in Applied Mathematics at Centre Borelli (ENS Paris-Saclay) under supervision of Nicolas Vayatis. I am working in collaboration with Framatome, a world leader in civil nuclear industry.
My work focuses on the control of complex dynamical systems under real-world constraints, combining machine learning and model-based approaches to improve both computational efficiency and robustness. I am particularly interested in bridging the gap between modern ML methods and control theory, with an emphasis on scalable and reliable solutions for industrial systems.
My main research topics include surrogate dynamics learning, imitation learning, reinforcement learning, and model predictive control.
All my publications are listed here and on my Google Scholar. For any request, feel free to contact me on my email adress.
Recent Works
Efficient Sampling of Trajectories for Online Finetuning of ML Surrogate Simulation Scheme (Starting May 2026)
Uncertainty Quantification for DAE Simulation Schemes (Starting Mar 2026)
In long-horizon nonlinear MPC, we show that learned surrogate dynamics can significantly accelerate computation while preserving safety.
This paper develops a ML surrogate simulation scheme for fast integration of Differential Algebraic Equations modeling a nuclear reactor core.
This paper develops two major improvements on the global optimization LIPO from Malherbe & Vayatis, 2017: an empirical stopping criterion and a decaying exploration rate.
Unifying the views of AdaBoost, in order to better understand its dynamics.
Office 3S28
ENS Paris-Saclay
Gif-sur-Yvette, France
perceval.beja-battais [at] ens-paris-saclay [dot] fr
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