Results 181 to 190 of about 82,239 (329)

High performance GPU implementation of KNN algorithm: A review. [PDF]

MethodsX
Bidye P, Borkar P, Rakesh N.
europepmc   +1 more source

Gpu, Model, Execution Time, Stencils, In-Plane, Cuda.

, 2016
Xiomara Ribero Figueroa
openalex   +1 more source

Cuda-based technology for improving the efficiency of the aircraft motion

, 2016
Serhii Tovkach
openalex   +1 more source

On the Performance and Convergence of PINNs for Problems in Linear Elasticity

Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.
ABSTRACT Physics‐informed neural networks (PINNs) have emerged as a promising approach for solving partial differential equations by embedding physical laws directly into the loss function. However, their performance characteristics for problems in computational mechanics remain insufficiently understood.
Dipraj Kadlag, Carolin Birk, Sundararajan Natarajan, Hauke Gravenkamp   +3 more
wiley   +1 more source

CCC-GPU: a graphics processing unit (GPU)-accelerated nonlinear correlation coefficient for large-scale transcriptomic analyses. [PDF]

Bioinformatics
Zhang H, Fotso KT, Subirana-Granés M, Pividori M.   +3 more
europepmc   +1 more source

Using CUDA architecture for computer simulations of thermomechanical phenomena [PDF]

, 2014
G. Michalski, Norbert Sczygiol, Siergiei Leonov   +2 more
openalex   +1 more source

Diffusion model‐regularized implicit neural representation for computed tomography metal artifact reduction

Quantitative Biology, Volume 14, Issue 2, June 2026.
Abstract Computed tomography (CT) images are often severely corrupted by artifacts in the presence of metals. Existing supervised metal artifact reduction (MAR) approaches suffer from performance instability on known data due to their reliance on limited paired metal‐clean data, which limits their clinical applicability. Moreover, existing unsupervised
Jie Wen, Chenhe Du, Xiao Wang, Yuyao Zhang   +3 more
wiley   +1 more source

NIRFASTerFF: an accessible, cross-platform Python package for fast photon modeling. [PDF]

J Biomed Opt
Cao J, Montero-Hernandez S, Mesquita RC, Eggebrecht AT, Dehghani H.   +4 more
europepmc   +1 more source

A Deep Learning Framework for Peak Ground Velocity Prediction Using Multi‐Station Velocity Waveforms: The Taiwan Transformer Shaking Alert Model (TT‐SAM)

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.
Abstract This study proposes a deep‐learning–based regional earthquake early warning model, the Taiwan Transformer Shaking Alert Model (TT‐SAM). The model adopts peak ground velocity (PGV) as its primary ground shaking prediction unit, aiming to better reflect actual structural damage and thereby enhance the practical utility and accuracy of the ...
Yu‐Heng Chen, Chung‐Han Chan, Chieh‐Chen Chang, Kuo‐Fong Ma   +3 more
wiley   +1 more source

Function inference of million-scale microbiomes using multi-GPU acceleration. [PDF]

Microbiol Spectr
Zhang Y, Wang M, Sun Y, Gao H, Su X.
europepmc   +1 more source