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Found 3 papers, 1 papers with code
Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel
To overcome this limitation, we propose a novel ROM framework that integrates optimal transport (OT) theory and neural network-based methods.
A graph convolutional autoencoder approach to model order reduction for parametrized PDEs
Here, we develop a non-intrusive and data-driven nonlinear reduction approach, exploiting GNNs to encode the reduced manifold and enable fast evaluations of parametrized PDEs.
An artificial neural network approach to bifurcating phenomena in computational fluid dynamics
This work deals with the investigation of bifurcating fluid phenomena using a reduced order modelling setting aided by artificial neural networks.
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