Convergence of symmetric kernel collocation for nonlinear PDEs is established in the RKHS norm for both fill-distance and residual-greedy point selection, without assuming solution uniqueness.
Solving and Learning Nonlinear PDEs with Gaussian Processes
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On Symmetric Kernel Collocation for Nonlinear PDEs
Convergence of symmetric kernel collocation for nonlinear PDEs is established in the RKHS norm for both fill-distance and residual-greedy point selection, without assuming solution uniqueness.