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SUMMARY:Uncertainty Quantification and Data Assimilation in Fluid Dynamics
DTSTART:20261111T133000Z
DTEND:20261113T121000Z
DTSTAMP:20261005T080400Z
UID:indico-event-191@indico.sissa.it
CONTACT:grozza@sissa.it
DESCRIPTION:Speakers: Lorenzo Tamellini (CNR-IMATI)\, Marcello Meldi (Arts
  et Métiers ParisTech)\, Gianluigi Rozza (SISSA\, International School fo
 r Advanced Studies)\n\n\nObjectives\nThe workshop is designed to foster in
 terdisciplinary dialogue and collaboration around the integration of data 
 assimilation (DA) and uncertainty quantification (UQ) techniques in the st
 udy and control of fundamental flow configurations and complex flows of in
 dustrial interest\, which is a central challenge in both academic research
  and industrial applications.\nFlow configurations relevant for industry a
 re inherently complex\, nonlinear\, and sensitive to initial and boundary 
 conditions. Despite significant advances\, the predictive power of computa
 tional fluid dynamics (CFD) in this context remains limited by uncertainti
 es in physical models\, numerical approximations\, and sparse or noisy exp
 erimental data.\nThis workshop aims to address these challenges by showcas
 ing how DA and UQ can be leveraged to improve the reliability\, accuracy\,
  and efficiency of CFD analysis in real-world engineering systems. A cruci
 al goal of this meeting is to foster interactions between applied mathemat
 icians\, computational scientists\, and engineers.\nWe welcome contributio
 ns in forms of short talks (20-30 minute) of any of these themes: \n\n\nA
 dvances in UQ and DA methodologies for CFD:  Discussing novel methodologi
 es to increase/strengthen e.g. the ability of DA to fuse experimental meas
 urements (e.g.\, particle image velocimetry\, pressure sensors) with CFD t
 o produce more accurate flow fields\, and/or the ability UQ in quantifying
  confidence levels in simulation outputs and guiding decision-making under
  uncertainty.\n\n\nIndustrial Relevance and Impact: Exploring applications
  in sectors such as aerospace (e.g.\, jet engines\, airframe aerodynamics)
 \, automotive (e.g.\, drag reduction\, combustion)\, energy (e.g.\, wind t
 urbines\, heat exchangers)\, and environmental engineering (e.g.\, polluta
 nt dispersion\, or heating\, ventilation\, and air conditioning systems)\,
  emphasizing how DA/UQ can support design optimization\, performance monit
 oring\, and risk assessment.\n\n\nData-Driven Modeling and Reduced-Order T
 echniques: Discussing the coupling of machine learning and reduced-order m
 odels with DA/UQ to enable online analysis and control\, especially in sce
 narios where full-scale simulations are expensive.\n\n\nExperimental Desig
 n and Sensor Placement: Presenting strategies for optimal sensor placement
  and experimental design to maximize the information gain from measurement
 s\, thereby improving the effectiveness of data assimilation and reducing 
 epistemic uncertainty.\n\n\nSoftware tools enabling technological transfer
  from research to engineering practice in CFD.\n\n\n\nOrganized by\n    
         \n \n\nhttps://indico.sissa.it/event/191/
LOCATION:Room 128-129 (SISSA)
URL:https://indico.sissa.it/event/191/
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