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We introduce hp-greedy, a refinement approach for building gravitational wavesurrogates as an extension of the standard reduced basis framework. Ourproposal is data-driven, with a domain decomposition of the parameter space,local reduced basis, and a binary tree as the resulting structure, which areobtained in an automated way. When compared to the standard global reducedbasis approach, the numerical simulations of our proposal show three salientfeatures: i) representations of lower dimension with no loss of accuracy, ii) asignificantly higher accuracy for a fixed maximum dimensionality of the basis,in some cases by orders of magnitude, and iii) results that depend on thereduced basis seed choice used by the refinement algorithm. We first illustratethe key parts of our approach with a toy model and then present a morerealistic use case of gravitational waves emitted by the collision of twospinning, non-precessing black holes. We discuss performance aspects ofhp-greedy, such as overfitting with respect to the depth of the tree structure,and other hyperparameter dependences. As two direct applications of theproposed hp-greedy refinement, we envision: i) a further acceleration ofstatistical inference, which might be complementary to focused reduced-orderquadratures, and ii) the search of gravitational waves through clustering andnearest neighbors.

An automated parameter domain decomposition approach for gravitational wave surrogates using hp-greedy refinement