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DTSTAMP:20210916T132452Z
LOCATION:Ernesto Bertarelli
DTSTART;TZID=Europe/Stockholm:20210708T120000
DTEND;TZID=Europe/Stockholm:20210708T123000
UID:submissions.pasc-conference.org_PASC21_sess160_msa158@linklings.com
SUMMARY:Efficient Multi-Level Monte Carlo Estimators for Robust Engineerin
 g Design
DESCRIPTION:Minisymposium\n\nEfficient Multi-Level Monte Carlo Estimators 
 for Robust Engineering Design\n\nGanesh, Ayoul-Guilmard, Nobile\n\nMeteoro
 logical conditions play an important role in civil engineering, particular
 ly for tall and slender structures. It is essentially difficult to predict
  at the design stage, and a popular approach is to use a stochastic model 
 based on past observations. An optimal design robust to the variability of
  these conditions is typically sought as the solution to a problem of opti
 misation under uncertainty, whose objective function features a risk measu
 re of a random variable. Since this random variable is usually expensive t
 o sample – e.g. requiring a large-scale simulation of fluid dynamics
  – an efficient and accurate method to estimate the risk measure and
  the statistics involved is paramount. We propose multi-level Monte Carlo 
 (MLMC) estimators for parametric expectations, from which risk measures of
  engineering interest (e.g. conditional value-at-risk) can be constructed.
  We also present associated a posteriori error estimators, and algorithms 
 to adaptively and continuously calibrate the MLMC estimator from the estim
 ated error. Finally, we present numerical results of performance and relia
 bility on problems inspired by wing engineering. This work has been implem
 ented in a Python library within a framework for high-performance computin
 g in distributed environments, in collaboration with the CIMNE and the Bar
 celona Supercomputing Center.\n\nDomain: CS and Math, Climate and Weather,
  Engineering
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