The ninth UQSay seminar on Uncertainty Quantification and related topics, organized by MSSMAT, L2S and EDF R&D, will take place online on Thursday afternoon, May 28, 2020.
Real-time data assimilation and control on mechanical systems under uncertainties
The work is placed into the framework of data assimilation and
control in structural mechanics. It aims at developing new
numerical tools in order to permit real-time and robust data
assimilation and control that could then be used in various
engineering activities. A specific targeted activity is the
implementation of the DDDAS (Dynamic Data Driven Application
System) technology in which a continuous exchange between simulation
tools and experimental measurements is envisioned to the end
of creating retroactive control loops on mechanical systems. In
this context, and in order to take various uncertainty sources
(modeling error, measurement noise…) into account, a powerful
and general stochastic methodology with Bayesian inference is
considered. However, a well-known drawback of such an approach is
the computational complexity which makes real-time simulations and
sequential assimilation some difficult tasks.
The presented work thus proposes to couple Bayesian inference with
attractive and advanced numerical techniques so that real-time and
sequential assimilation can be envisioned. First, PGD model
reduction is introduced to facilitate the computation of the
likelihood function, the uncertainty propagation through complex
models, and the sampling of the posterior density. PGD builds a
multi-parametric solution in an offline phase and leads to cost
effective evaluation of the numerical model depending on
parameters in the online inversion phase. Second, Transport Map
sampling is investigated as a substitute to classical MCMC
procedures for posterior sampling. It is shown that this technique
leads to deterministic computations, with clear convergence
criteria, and that it is particularly suited to sequential data
assimilation. Here again, the use of PGD model reduction highly
facilitates the process by recovering gradient and Hessian
information in a straightforward manner. Third, and to increase
robustness, on-the-fly correction of model bias is addressed in a
stochastic context using data-based enrichment terms. Eventually,
the synthesis of control laws in a stochastic context, using both
PGD model reduction and dynamically updated model parameters, is
investigated into the DDDAS framework in order to drive the
physical system accordingly.
The overall methodology is applied and illustrated on specific
test-cases dealing with 1the control of fusion welding processes
or the management of mechanical tests on damageable concrete
structures equipped with full-field measurements.
Joint work Paul-Baptiste Rubio and François Louf.
Refs: j.crme.2019.11.004, nme.6143, and s00466-018-1575-8.
PDF abstract: uqsay09_abstract_lchamoin.pdf.
Organizers: Julien Bect (L2S), Emmanuel Vazquez (L2S), Didier Clouteau (MSSMAT), Filippo Gatti (MSSMAT), Fernando Lopez Caballero (MSSMAT), Bertrand Iooss (EDF R&D).
Practical details: the seminar will be held online using Microsoft Teams.
If you want to attend this seminar (or any of the forthcoming online UQSay seminars), simply send an email to julien.bect@centralesupelec.fr and you will be invited to the UQSay channel on Teams. You will find the link to the seminar on this channel, approximately 15 minutes before the beginning.
The technical side of things: you can use Teams either directly from you web browser or using the "fat client" application, which is available for most platforms (Windows, Linux, Mac, Android & iOS). We recommend the latter option whenever possible. Please give it a try before the seminar to anticipate potential problems.
