arXiv:2601. 20174v3 Announce Type: replace-cross Abstract: Solving large-scale sparse linear systems originating from partial differential equations (PDEs) is a fundamental topic in high-performance scientific computing, where preconditioners are crucial.
By Alexander Benanti, Xi Han, Hong Qin
arXiv:2605. 26854v2 Announce Type: replace Abstract: The scalable solution of large sparse linear systems is a bottleneck in scientific computing and graph analysis.
By Yali Fink, Ido Ben-Yair, Lars Ruthotto, Eran Treister
The paper presents a neural hierarchical‑matrix preconditioner designed for real‑time GPU solves of sparse symmetric positive‑definite systems that change each frame. By training a graph‑and‑attention network to predict an SPD approximate inverse in H²‑matrix format, the method achieves linear‑time inference and application, outperforming traditional multigrid setup times and local preconditioners. Experiments on 3D mesh diffusion problems show the preconditioner reduces conjugate‑gradient iterations from 116 to 33 and enables 120 fps real‑time performance for up to 3,647 unknowns.
By Carl Osborne, Minghao Guo, Crystal Owens, Wojciech Matusik
arXiv:2606. 19251v1 Announce Type: cross Abstract: Solving the pressure-Poisson equation remains the primary computational bottleneck in incompressible unstructured flow solvers primarily due to the inherent sensitivity of traditional linear solvers to mesh irregularities.
By Eric Chill\'on, Artur K. Lidtke, Nguyen Anh Khoa Doan, Bernat Font
arXiv:2509. 10378v2 Announce Type: replace-cross Abstract: Linear systems arise in generating samples and in calculating observables in lattice quantum chromodynamics~(QCD).
By Yixuan Sun, Srinivas Eswar, Yin Lin, William Detmold, Phiala Shanahan, Xiaoye Li, Yang Liu, Prasanna Balaprakash
arXiv:2509. 08765v4 Announce Type: replace-cross Abstract: Data-driven acceleration of scientific computing workflows has been a high-profile aim of machine learning (ML) for science, with numerical simulation of transient partial differential equations (PDEs) being one of the main applications.
By Mikhail Khodak, Min Ki Jung, Brian Wynne, Edmond Chow, Egemen Kolemen