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UID:8706@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20250701T110000
DTEND;TZID=Europe/Paris:20250701T120000
DTSTAMP:20250623T111649Z
URL:https://www.i2m.univ-amu.fr/evenements/non-linear-model-order-reductio
 n-of-transport-dominated-problems-with-shifted-proper-orthogonal-decomposi
 tion-spod-using-machine-learning-methods/
SUMMARY:Shubhaditya Burela (...): Non-linear model order reduction of trans
 port-dominated problems with shifted proper orthogonal decomposition (sPOD
 ) using machine learning methods.
DESCRIPTION:Shubhaditya Burela: Parametric model order reduction techniques
  often struggle to capture transport-dominated phenomena because the Kolmo
 gorov n-width decays slowly. To tackle this issue\, we introduce a data-dr
 iven method that combines sPOD with deep learning. First\, we use sPOD to 
 create a high-fidelity\, low-dimensional model of the system\, which then 
 feeds into a deep learning network to predict how the system behaves under
  different parameters.\nOne challenge with sPOD is that it needs the shift
 s to be known beforehand. To address this\, we also propose a neural netwo
 rk approach that learns both the shifts and the co-moving low-rank fields 
 at the same time by using two specialized sub-networks. This is a first st
 ep towards an automated sPOD method\, and we are also exploring how PINN-s
 tyle learning can help improve the separation of the transport field and t
 he detection of shifts.
CATEGORIES:Séminaire,Analyse Appliquée
LOCATION:I2M Saint-Charles - Salle de séminaire\, Université Aix-Marseill
 e\, Campus Saint-Charles\, 3 Place Victor Hugo\, Marseille\, 13003\, Franc
 e
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Université Aix-Marseille\,
  Campus Saint-Charles\, 3 Place Victor Hugo\, Marseille\, 13003\, France;X
 -APPLE-RADIUS=100;X-TITLE=I2M Saint-Charles - Salle de séminaire:geo:0,0
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DTSTART:20250330T030000
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