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DTSTAMP:20250822T115808Z
LOCATION:Room 5.0B56
DTSTART;TZID=Europe/Stockholm:20250617T160000
DTEND;TZID=Europe/Stockholm:20250617T163000
UID:submissions.pasc-conference.org_PASC25_sess125_msa197@linklings.com
SUMMARY:Scaling Machine Learning Methods Towards Industrial-Grade Aircraft
  Aerodynamics Applications
DESCRIPTION:Philipp Bekemeyer (DLR)\n\nIn areas for which vast amounts of 
 data are available Machine learning and artificial intelligence techniques
  had a tremendous success, especially when mathematical models are lacking
 . Instead, engineering tools in general and computational fluid dynamics t
 ools in particular rely on first-order principals that directly enable to 
 describe and investigate system behavior. However, such tools are far from
  perfect and suffer several short-comings, e.g. computational bottlenecks 
 once a massive amount of simulations is required or the problem of derivin
 g accurate turbulence models to describe small scale turbulent behavior. M
 achine learning techniques are generally regarded as a possibility to enha
 nce and complement first-order based numerical simulation tools to circumv
 ent these shortcomings. Following this ambition, the Center for Computer A
 pplications in AeroSpace Science and Engineering department of the German 
 Aerospace Center investigates scientific machine learning techniques in cl
 ose connection to established numerical simulation tools as well as indust
 rial needs. This presentation will provide an insight into previous and cu
 rrent activities within the department covering topics from purely data-dr
 iven approaches to the incorporation of physical knowledge into models spe
 cifically highlighting challenges that arise when looking at large-scale i
 ndustrial configuration and integration into existing, traditional workflo
 ws.\n\nDomain: Engineering, Computational Methods and Applied Mathematics\
 n\nSession Chair: Neil Ashton (NVIDIA Inc.)\n\n
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