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Found 4 papers, 1 papers with code
Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning
Through a series of benchmark problems of natural convection in porous media, BT-AE performs better than the previous DL-ROM framework by providing comparable results to POD-based approaches for problems where the solution lies within a linear subspace as well as DL-ROM autoencoder-based techniques where the solution lies on a nonlinear manifold; consequently, bridges the gap between linear and nonlinear reduced manifolds.
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.
Projection-based reduced order models for a cut finite element method in parametrized domains
This work presents a reduced order modelling technique built on a high fidelity embedded mesh finite element method.
Numerical Analysis Numerical Analysis 78M34, 97N40, 35Q35
Combined parameter and model reduction of cardiovascular problems by means of active subspaces and POD-Galerkin methods
In this chapter we introduce a combined parameter and model reduction methodology and present its application to the efficient numerical estimation of a pressure drop in a set of deformed carotids.
Numerical Analysis
