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DTSTAMP:20250822T115812Z
LOCATION:Campussaal - Plenary Room
DTSTART;TZID=Europe/Stockholm:20250617T090000
DTEND;TZID=Europe/Stockholm:20250617T100000
UID:submissions.pasc-conference.org_PASC25_sess162_plen103@linklings.com
SUMMARY:ID01 - Learning to Fly: Harnessing High-Performance Computing and 
 Machine Learning for Sustainable Aviation
DESCRIPTION:Paola Cinnella (Sorbonne University) and Matej Praprotnik (Nat
 ional Institute of Chemistry)\n\nAdvances in high-performance computing (H
 PC) enable the simulation of complex, multiscale flow phenomena relevant t
 o aerospace with unprecedented accuracy, generating vast high-fidelity dat
 asets. In parallel, scientific machine learning (SML) is rapidly transform
 ing the physical sciences. When combined, SML and HPC promise a step chang
 e in predictive capabilities and computational efficiency for CFD, with po
 tential impact on decarbonizing aviation and other carbon-intensive sector
 s. Yet, key challenges remain: selecting informative training data, genera
 lizing to out-of-distribution conditions, and quantifying predictive uncer
 tainty. In this talk, I will explore how the synergy between HPC and SML c
 an be harnessed to accelerate sustainable aviation, and highlight current 
 bottlenecks and research directions toward trustworthy, scalable ML-enhanc
 ed CFD.\n\nSession Chair: Peter Vincent (Imperial College London)\n\n
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