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Non-intrusive Sparse Subspace Learning for Parametrized Problems

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Figure

Fig. 1: Schematics for the 2D lid driven cavity flow.
Fig. 2: Heatmap plot showing the magnitude of both sampling points M i j and rep- rep-resentation coe ffi cients S i j , evidencing the sparsity of the solution of the parametric lid-driven cavity flow in the space spanned by hierarchical polynomial functi
Fig. 3: The norm of the sparse surplus functions s(x, µ j ) decreases with the refine- refine-ment in the parametric space
Fig. 4: The surplus norm of surplus functions s(x, µ j ) correlates linearly with the amount of computational work W j required to compute direct solutions M i j using the predictions ¯ M i j as initial guess in the nonlinear solver
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