Évènements / Séminaires

Évènements à venir

“Séminaire d’Automatique du plateau de Saclay”

The next seminar of the H-CODE series “Séminaire d’Automatique du plateau de Saclay” will take place on Thursday, April 21st The seminar will be preceded by a coffee break with cakes in the coffee room on the 3rd floor, and consist of two talks.

21/04/2026

Seminars Energy@L2S: Tuesday, April 21, 2026 10:30am Salle Hooper

Dear all, We kindly invite you to the new seminar of the cycle related to the interdisciplinary research line (axe transverse) Energie at L2S, that will be held physically in Salle Hooper at 10:30am of Tuesday 21st April 2026. Please find the details of the seminar at the end of the email.Best regardsAlessio Speaker:  Petr Vorobev, […]

21/04/2026

11e Journée Statistique et Informatique pour la Science des Données – Vendredi 3 avril 2026

Cet événement rassemblera des chercheurs en mathématiques et en informatique autour de sujets comme l’apprentissage automatique, l’optimisation, le transport optimal, et bien d’autres. Au programme : des interventions de Richard Combes, Hugo Cui, Ekhine Irurozki, Anna Korba, Paul Mangold et Tabea Rebafka.

03/04/2026

UQSay #97 — Anthony Quintin — Optimal experimental designs based on the cross-entropy method for planning fracture toughness tests

The ninety-seventh UQSay seminar on UQ, DACE and related topics will take place online on Thursday afternoon, March 26, 2026. 2–3 PM — Anthony Quintin (CEA DIF) Optimal experimental designs based on the cross-entropy method for planning fracture toughness tests Nuclear reactor pressure vessels undergo progressive embrittlement under neutron irradiation, monitored through the Master Curve […]

26/03/2026

The next seminar of the H-CODE series “Séminaire d’Automatique du plateau de Saclay” will take place on Friday, 13 march at 11h00 at L2S

Title: Robust Stability and Recurrence of Stochastic Optimal Control Abstract: A Lyapunov theoretic approach to stability and robustness of optimal control is well-established for deterministic systems. Considering the rise of learning in control, the close links between optimal control and reinforcement learning (RL), as well as the fact that RL is usually formulated for stochastic […]

13/03/2026