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Sampling from face-averages of cells is now in the toolkit, but a test needs to be created to exercise/protect this capability.
1.) A 2D spatial domain provided average values which reconstructs point evaluations (for testing they capture the solution to machine precision) and also with the face averaging target functional (for testing that it matches the input data to machine precision).
2.) A 2D manifold domain in 3 space. Because there is no curvature, machine precision reconstruction of polynomial functions should be possible. An alternative is another Python convergence test on a domain such as the surface of a sphere.
Because of the need to access cell-centered data, it is most appropriate that these tests be performed in the harness.
The text was updated successfully, but these errors were encountered:
Sampling from face-averages of cells is now in the toolkit, but a test needs to be created to exercise/protect this capability.
1.) A 2D spatial domain provided average values which reconstructs point evaluations (for testing they capture the solution to machine precision) and also with the face averaging target functional (for testing that it matches the input data to machine precision).
2.) A 2D manifold domain in 3 space. Because there is no curvature, machine precision reconstruction of polynomial functions should be possible. An alternative is another Python convergence test on a domain such as the surface of a sphere.
Because of the need to access cell-centered data, it is most appropriate that these tests be performed in the harness.
The text was updated successfully, but these errors were encountered: