9 citations · 9 across the 4 of their papers we have counts for
5 papers
Structured Representation Learning for Behavior Cloning: How can we learn to safely control a nuclear power plant?
Perceval Beja-Battais, Alain Grosset{ê}te, Nicolas Vayatis
Learned models for industrial control are usually judged by aggregate accuracy, but accuracy at the component level does not guarantee safety once it is embedded in the system it i…
Accelerating Full-Scale Nonlinear Model Predictive Control via Surrogate Dynamics Optimization
Perceval Beja-Battais, Guillaume Dupré, Alain Grossetête +1
Driven by advances in hardware and software technologies, nonlinear model predictive control (NMPC) has gained increasing adoption in both industry and academia over the past decad…
Enhancing Nuclear Reactor Core Simulation through Data-Based Surrogate Models
Perceval Beja-Battais, Alain Grossetête, Nicolas Vayatis
In recent years, there has been an increasing need for Nuclear Power Plants (NPPs) to improve flexibility in order to match the rapid growth of renewable energies. The Operator Ass…
LIPO+: Frugal Global Optimization for Lipschitz Functions
Gaëtan Serré, Perceval Beja-Battais, Sophia Chirrane +2
In this paper, we propose simple yet effective empirical improvements to the algorithms of the LIPO family, introduced in [Malherbe2017], that we call LIPO+ and AdaLIPO+. We compar…
Overview of AdaBoost : Reconciling its views to better understand its dynamics
Perceval Beja-Battais
Boosting methods have been introduced in the late 1980's. They were born following the theoritical aspect of PAC learning. The main idea of boosting methods is to combine weak lear…