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Found 2 papers, 2 papers with code
Data driven approximation of parametrized PDEs by Reduced Basis and Neural Networks
We are interested in the approximation of partial differential equations with a data-driven approach based on the reduced basis method and machine learning.
Coupling non-conforming discretizations of PDEs by spectral approximation of the Lagrange multiplier space
Differently from the mortar method, we discretize the space of basis functions on the interface by spectral approximation independently of the discretization of the two adjacent domains; one of the possible choices is to approximate the interface variational space by Fourier basis functions.
Numerical Analysis Computational Engineering, Finance, and Science Numerical Analysis
