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. My PhD is funded through a CIFRE fellowship with Framatome, a world leader in civil nuclear industry. My research sits at the intersection of machine learning and optimal control, with a focus on developing methods that are both faster and more accurate in demanding industrial settings. I am particularly interested in how modern ML frameworks can address the computational challenges that arise in real-world control problems.
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.
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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