Évènements

Évènements à venir

On Power Systems Stability Definitions and Analysis: the DC Microgrid example

Tuesday 26th January, 14:00 – 15:00 – Alessio Iovine, CNRS researcher at L2S  Title: On Power Systems Stability Definitions and Analysis: the DC Microgrid example Click here to join the meeting Abstract: The problem of defining and classifying power system stability has been addressed several times in the scientific literature. However, only recently the exchanges between the Control and Power […]

26/01/2021

UQSay #22

The twenty-second UQSay seminar on UQ, DACE and related topics, organized by L2S, MSSMAT, LMT and EDF R&D, will take place online on Thursday afternoon, January 21, 2021. 14h–15h — Cédric Travelletti (University of Bern) Implicit Update for Large-Scale Inversion under GP prior We present an almost matrix-free update method for posterior Gaussian process distributions […]

21/01/2021

Séminaire de Andrea Simonetto

19/01/2021 – 14h00-15h00 – Online Personalized optimization for cyber-physical and social systems Andrea Simonetto (IBM Research Ireland) Abstract. Optimization is the cornerstone of many engineering systems and cyber-physical systems including smart homes, energy grids, and intelligent transportation systems. In many situations however, state-of-the-art optimization algorithms may fail to provide acceptable (and feasible) solutions e.g. because […]

19/01/2021

UQSay #20

The twentieth UQSay seminar on UQ, DACE and related topics, organized by L2S, MSSMAT, LMT and EDF R&D, will take place online on Thursday afternoon, December 17, 2020. 14h–15h — Bojana Rosic (University of Twente, Netherlands) Inverse methods for damage estimation in concrete given small data sets One of the main issues in material science […]

17/12/2020

Efficient MCMC sampling via asymptotically exact data augmentation

Speaker — Maxime Vono (IRIT — INP-ENSEEIHT) https://s3-seminar.github.io/seminars/maxime-vono/ Abstract — Performing exact Bayesian inference for complex models is computationally intractable. Markov chain Monte Carlo (MCMC) algorithms can provide reliable approximations of the posterior distribution but are expensive for large datasets and high-dimensional models. A standard approach to mitigate this complexity consists in using subsampling techniques […]

11/12/2020