Results 181 to 190 of about 9,194 (254)

Physics‐Informed Reservoir Characterization From Bulk and Extreme Pressure Events With a Differentiable Simulator

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Accurate characterization of subsurface heterogeneity is challenging but essential for applications such as reservoir pressure management, geothermal energy extraction and CO2 ${\text{CO}}_{2}$, H2 ${\mathrm{H}}_{2}$, and wastewater injection operations.
Harun Ur Rashid, Mingxin Li, Aleksandra Pachalieva, Georg Stadler, Daniel O’Malley   +4 more
wiley   +1 more source

Predicting microbe-disease association based on graph autoencoder and inductive matrix completion with multi-similarities fusion. [PDF]

Front Microbiol
Shi K, Huang K, Li L, Liu Q, Zhang Y, Zheng H.   +5 more
europepmc   +1 more source

A Self‐Supervised Framework for Space Object Behaviour Characterisation

Expert Systems, Volume 43, Issue 10, October 2026.
ABSTRACT Foundation Models, which leverage large neural networks pre‐trained on unlabelled data before fine‐tuning for specific tasks, are increasingly being applied to specialised domains. Recent examples include ClimaX for climate and Clay for satellite Earth observation, but a Foundation Model for Space Object Behavioural Analysis (SOBA) has not yet
Ian Groves, Andrew Campbell, James Fernandes, Diego Ramírez Rodríguez, Paul Murray, Massimiliano Vasile, Victoria Nockles   +6 more
wiley   +1 more source

Bearing fault detection by using graph autoencoder and ensemble learning. [PDF]

Sci Rep
Wang M, Yu J, Leng H, Du X, Liu Y.
europepmc   +1 more source

GEMA: Graph Embeddings for Multi‐Agent Coordination

AI Magazine, Volume 47, Issue 3, Fall 2026.
Abstract Many cooperative multi‐agent tasks are naturally defined by graph‐structured objectives, where agents must collectively reach, for example, a desired relational configuration or satisfy a set of constraints. These objectives often encode spatial arrangements, inter‐agent relations, or constraints that can be formalized as target graphs ...
Alessandro Amato, Raffaele Galliera, K. Brent Venable, Niranjan Suri   +3 more
wiley   +1 more source

Identification of microbe-disease signed associations via multi-scale variational graph autoencoder based on signed message propagation. [PDF]

BMC Biol
Zhu H, Hao H, Yu L.
europepmc   +1 more source

Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling

Advanced Materials, Volume 38, Issue 51, 11 September 2026.
AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll, Ivan Pinto‐Huguet, Enzo Rotunno, Thomas Galvani, Catalina Coll, Payam Habibzadeh Kavkani, Maria Chiara Spadaro, Yann‐Michel Niquet, Martin Børstad Eriksen, Sara Martí‐Sánchez, Georgios Katsaros, Giordano Scappucci, Peter Krogstrup, Giovanni Isella, Andreu Cabot, Gonzalo Merino, Pablo Ordejón, Stephan Roche, Vincenzo Grillo, Jordi Arbiol   +19 more
wiley   +1 more source

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection. [PDF]

Sci Rep
Ren Z, Li X, Peng J, Chen K, Tan Q, Wu X, Shi C.   +6 more
europepmc   +1 more source

Accurately Deciphering Tissue Heterogeneity From Spatial Multi‐Modal and Multi‐Omics With STransformer

Advanced Science, Volume 13, Issue 49, 3 September 2026.
STransformer is a unified deep learning framework designed to seamlessly accommodate a comprehensive landscape of spatial data. By simultaneously capturing short‐range cellular interactions and tissue‐wide semantic patterns, it extracts robust representations to accurately dissect complex tissue heterogeneity.
Xingyi Li, Jialuo Xu, Gaoyuan Du, Xiangting Jia, Dongmin Zhao, Chunyan Zhou, Kexin Xiao, Jia Gu, Junnan Zhu, Xuequn Shang   +9 more
wiley   +1 more source

Deciphering spatial domains from spatially resolved transcriptomics with Siamese graph autoencoder. [PDF]

Gigascience
Cao L, Yang C, Hu L, Jiang W, Ren Y, Xia T, Xu M, Ji Y, Li M, Xu X, Li Y, Zhang Y, Fang S.   +12 more
europepmc   +1 more source