Results 71 to 80 of about 5,698,498 (295)

Data‐Driven Materials Science for Energy‐Sustainable Applications

Advanced Materials, EarlyView.
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley   +1 more source

Scalable Graph Convolutional Networks With Fast Localized Spectral Filter for Directed Graphs

IEEE Access, 2020
Graph convolutional neural netwoks (GCNNs) have been emerged to handle graph-structured data in recent years. Most existing GCNNs are either spatial approaches working on neighborhood of each node, or spectral approaches based on graph Laplacian ...
Chensheng Li, Xiaowei Qin, Xiaodong Xu, Dujia Yang, Guo Wei   +4 more
doaj   +1 more source

On‐Chip Photonic Neural Network Architectures

Advanced Optical Materials, EarlyView.
This review presents a comprehensive overview of on‐chip photonic neural network architectures, covering key photonic building blocks, representative network types, and emerging applications. Recent advances, implementation challenges, and future directions are examined, highlighting the potential of integrated photonics to enable ultrafast, energy ...
Seokjin Hong, Berkay Neseli, Seungyeon Cho, Seungsoo Lee, Yeonjun Kim, Seungyoon Choi, Hyo‐Hoon Park, Hamza Kurt   +7 more
wiley   +1 more source

Graph neural network method for the intelligent selection of river system

Geocarto International, 2023
The spatial features and generalisation rules for river network generalisation are difficult to directly quantify using indicators. To consider dimensional information hidden in river networks and improve river network selection accuracy, this study ...
Di Wang, Haizhong Qian
doaj   +1 more source

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

, 2020
Modeling complex spatial and temporal correlations in the correlated time series data is indispensable for understanding the traffic dynamics and predicting the future status of an evolving traffic system.
Bai, Lei, Wang, Can, Yao, Lina, Wang, Xianzhi, Li, Can   +4 more
core  

Multimodal Engagement Assessment in Children During Invented Story Paradigm With a Social Robot

Advanced Robotics Research, EarlyView.
A multimodal framework is proposed to assess children's engagement during storytelling interactions with a social robot. Gaze, physiological, and behavioral data are combined and validated against observer ratings. An automated gaze‐labeling strategy is introduced, and supervised classifiers achieve high accuracy. The study supports scalable engagement
Laura Fiorini, Elena Adelucci, Stefano Scatigna, Lorenzo Pugi, Alice Bruni, Benedetta Carotenuto, Chiara Pecini, Filippo Cavallo   +7 more
wiley   +1 more source

A review on the applications of graph neural networks in materials science at the atomic scale

Materials Genome Engineering Advances
In recent years, interdisciplinary research has become increasingly popular within the scientific community. The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from ...
Xingyue Shi, Linming Zhou, Yuhui Huang, Yongjun Wu, Zijian Hong   +4 more
doaj   +1 more source

Gated Graph Convolutional Recurrent Neural Networks [PDF]

2019 27th European Signal Processing Conference (EUSIPCO), 2019
Graph processes model a number of important problems such as identifying the epicenter of an earthquake or predicting weather. In this paper, we propose a Graph Convolutional Recurrent Neural Network (GCRNN) architecture specifically tailored to deal with these problems.
Luana Ruiz, Fernando Gama, Alejandro Ribeiro   +2 more
openaire   +3 more sources

Co-embedding of edges and nodes with deep graph convolutional neural networks

Scientific Reports, 2023
Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean data and have been widely used in various fields. However, most of the existing GNN models face two main challenges: (1) Most GNN models built upon the message-passing
Yuchen Zhou, Hongtao Huo, Zhiwen Hou, Lingbin Bu, Jingyi Mao, Yifan Wang, Xiaojun Lv, Fanliang Bu   +7 more
doaj   +1 more source

Data‐Driven Bulldozer Blade Control for Autonomous Terrain Leveling

Advanced Robotics Research, EarlyView.
A simulation‐driven framework for autonomous bulldozer leveling is presented, combining high‐fidelity terramechanics simulation with a neural‐network‐based reduced‐order model. Gradient‐based optimization enables efficient, low‐level blade control that balances leveling quality and operation time.
Harry Zhang, Ganesh Arivoli, Huzaifa Unjhawala, Luning Bakke, Radu Serban, Dan Negrut   +5 more
wiley   +1 more source