Projection-tree reduced order modeling for fast N-body computations

TL;DR

This paper introduces a novel data-driven reduced-order modeling framework called projection-tree ROM that significantly accelerates N-body simulations by combining hierarchical decomposition, dimensional reduction, and hyper-reduction techniques, achieving over 2000x speed-up.

Contribution

The paper proposes the projection-tree reduced order model (PTROM), integrating Barnes-Hut, LSPG, and GNAT methods to drastically reduce computational complexity in N-body problems.

Findings

Achieves over 2000x wall-time speed-up compared to full models.

Maintains less than 0.1% error in quantities of interest.

Operational count complexity is independent of the number of bodies N.

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.