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• W3134718908 abstract "This work presents a data-driven reduced-order modeling framework to accelerate the computations of $N$-body dynamical systems and their pair-wise interactions. The proposed framework differs from traditional acceleration methods, like the Barnes-Hut method, which requires online tree building of the state space, or the fast-multipole method, which requires rigorous $a$ $priori$ analysis of governing kernels and online tree building. Our approach combines Barnes-Hut hierarchical decomposition, dimensional compression via the least-squares Petrov-Galerkin (LSPG) projection, and hyper-reduction by way of the Gauss-Newton with approximated tensor (GNAT) approach. The resulting $projection-tree$ reduced order model (PTROM) enables a drastic reduction in operational count complexity by constructing sparse hyper-reduced pairwise interactions of the $N$-body dynamical system. As a result, the presented framework is capable of achieving an operational count complexity that is independent of $N$, the number of bodies in the numerical domain. Capabilities of the PTROM method are demonstrated on the two-dimensional fluid-dynamic Biot-Savart kernel within a parametric and reproductive setting. Results show the PTROM is capable of achieving over 2000$times$ wall-time speed-up with respect to the full-order model, where the speed-up increases with $N$. The resulting solution delivers quantities of interest with errors that are less than 0.1$%$ with respect to full-order model." @default.
• W3134718908 created "2021-03-15" @default.
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• W3134718908 date "2022-06-01" @default.
• W3134718908 modified "2023-09-26" @default.
• W3134718908 title "Projection-tree reduced-order modeling for fast N-body computations" @default.
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• W3134718908 doi "https://doi.org/10.1016/j.jcp.2022.111141" @default.
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