Over-sampling with norming sets stabilizes the Kansa collocation matrix for elliptic PDEs on spheres and yields proven error estimates for least-squares and QR-thinned square systems.
H 2-convergence of least-squares kernel collocation methods
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Extending Data to Improve Stability and Error Estimates Using Asymmetric Kansa-like Methods to Solve PDEs
Over-sampling with norming sets stabilizes the Kansa collocation matrix for elliptic PDEs on spheres and yields proven error estimates for least-squares and QR-thinned square systems.