Results 231 to 240 of about 82,239 (329)

gpu-cuda-spinodal-9pt

, 2017
Trevor Keller
openalex   +1 more source

How to grow new applications out of old research? Evidence from firm cumulative investments in deep learning

Strategic Management Journal, Volume 47, Issue 5, Page 1333-1367, May 2026.
Abstract Research Summary Firm technological research has the potential to spawn multiple applications. Despite recognizing such potential, past literature disagrees on the process through which firms discover and grow new applications out of their past technological research.
Xirong (Subrina) Shen
wiley   +1 more source

Fast barcode calling based on <i>k</i>-mer distances. [PDF]

PNAS Nexus
Uphoff RC, Schüler S, Grosse I, Müller-Hannemann M.   +3 more
europepmc   +1 more source

Dynamic Configuration of CUDA Runtime Variables for CDP-based Divide-and-Conquer Algorithms [PDF]

, 2019
Tiago Carneiro, Jan Gmys, Nouredine Melab, Fransisco Heron de Carvalho Junior, Pedro Pedrosa Rebouças Filho, Daniel Tuyttens   +5 more
openalex  

A Fast Calculation Scheme for Microwave Gas Absorption and Atmospheric Transmittance With Coupled Physical Constraints Deep Learning Method

Earth and Space Science, Volume 13, Issue 5, May 2026.
Abstract Accurate and efficient fast calculation of microwave atmospheric transmittance is the key to ensuring the accuracy of microwave radiative transfer simulations, and also an indispensable critical link in satellite microwave remote sensing and data assimilation.
H. Y. Huang, Yi‐Ning Shi, Y. Han, H. Hu, J. Yang, F. Z. Weng   +5 more
wiley   +1 more source

Global optimization tailored for graphics processing units: Complete and rigorous search for large-scale nonlinear minimization. [PDF]

PNAS Nexus
Zhang G, Shan Q, Cagan J.
europepmc   +1 more source

Crowdsourcing the Frontier: Advancing Hybrid Physics‐ML Climate Simulation via a $50,000 Kaggle Competition

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 5, May 2026.
Abstract Subgrid machine‐learning (machine learning [ML]) parameterizations have the potential to introduce a new generation of climate models that incorporate the effects of higher‐resolution physics without incurring the prohibitive computational cost associated with more explicit physics‐based simulations.
Jerry Lin, Zeyuan Hu, Tom Beucler, Katherine Frields, Hannah Christensen, Walter Hannah, Helge Heuer, Peter Ukkonen, Laura A. Mansfield, Tian Zheng, Liran Peng, Ritwik Gupta, Pierre Gentine, Yusef Al‐Naher, Mingjiang Duan, Kyo Hattori, Weiliang Ji, Chunhan Li, Kippei Matsuda, Naoki Murakami, Shlomo Ron, Marec Serlin, Hongjian Song, Yuma Tanabe, Daisuke Yamamoto, Jianyao Zhou, Mike Pritchard   +26 more
wiley   +1 more source

Transformer and graph variational autoencoder to identify microenvironments: A deep learning protocol for spatial transcriptomics. [PDF]

STAR Protoc
Paniagua K, Huang Y, Gao SJ, Chen Y, Jin YF, Flores M.   +5 more
europepmc   +1 more source

Accelerating Garfield++ with CUDA [PDF]

T. Neep, K. Nikolopoulos, Michael Slater
openalex   +1 more source

A Fully Connected Neural Network for Fast Estimation of Quasi‐Linear Diffusion Coefficients

Space Weather, Volume 24, Issue 5, May 2026.
Abstract Wave–particle interactions are a fundamental driver of electron radiation belt dynamics. Quantifying their effects through quasi‐linear theory requires diffusion coefficients, but their direct evaluation involves nested integrations and is computationally expensive, limiting their use in real‐time applications.
Mengli Tan, Yifan Wu, Zeyu An, Peng Peng, Xin Tao   +4 more
wiley   +1 more source