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Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Kolmogorov n-width Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel

Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Kolmogorov n-width Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel

26 August 2023
M. Khamlich
F. Pichi
G. Rozza
ArXivPDFHTML

Papers citing "Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Kolmogorov n-width Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel"

2 / 2 papers shown
Title
GFN: A graph feedforward network for resolution-invariant reduced
  operator learning in multifidelity applications
GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications
Oisín M. Morrison
F. Pichi
J. Hesthaven
AI4CE
21
1
0
05 Jun 2024
Neural empirical interpolation method for nonlinear model reduction
Neural empirical interpolation method for nonlinear model reduction
Max Hirsch
F. Pichi
J. Hesthaven
27
1
0
05 Jun 2024
1