Compressed Multiscale Kernel Collocation and Physics-Informed Kernel Learning for Elliptic BVPs

Abstract

Kernel collocation is a meshfree method for partial differential equations whose accuracy depends on the smoothness of the kernel and of the solution. For globally supported kernels, however, the collocation matrices are dense and severely ill-conditioned. We address both issues by combining a multiscale collocation scheme with samplets, a multiresolution basis of discrete signed measures with vanishing moments. In the samplet basis, the collocation matrix has only O(N log N) relevant entries, while the multiscale scheme adapts the kernel width to the spacing of the points on every level. Since the differential operator scales the equations at the interior points differently from those at the boundary points, the rescaling alone does not bound the condition number of the level systems. We therefore solve them by a Schur-complement reduction that is invariant under this scaling. For noisy data, we consider physics-informed kernel learning, which replaces the collocation system by a regularized least-squares fit. We show that it reduces to collocation as the regularization vanishes, and we solve it by a deflated conjugate gradient method that reuses the Schur-complement reduction. Numerical experiments in two and three dimensions, with exact and noisy boundary data, show that the method handles significantly more points than dense collocation.