Results 111 to 120 of about 8,113 (297)

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

A Generalized Spatio-Temporal Causal Knowledge Graph Framework With Large Language Model-Enhanced Reasoning for Urban Event Management

IEEE Access
Urban event management faces critical challenges in processing unstructured citizen complaints because of the limitations of traditional extraction models in handling colloquial text, static nature of conventional knowledge graphs that overlook ...
Gang Zheng
doaj   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

The application of classical conditioning to the machine learning of a commonsense knowledge of visual events [PDF]

, 2013
In the field of artificial intelligence, possession of commonsense knowledge has long been considered to be a requirementto construct a machine that possesses artificial general intelligence.
Furze, Timothy Andrew
core  

Knowledge Distillation for Temporal Knowledge Graph Reasoning with Large Language Models

CoRR
Reasoning over temporal knowledge graphs (TKGs) is fundamental to improving the efficiency and reliability of intelligent decision-making systems and has become a key technological foundation for future artificial intelligence applications. Despite recent progress, existing TKG reasoning models typically rely on large parameter sizes and intensive ...
Wang Xing, Wei Song, Siyu Lin, Chen Wu, Zhesi Li, Man Wang   +5 more
openaire   +2 more sources

Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art

Advanced Intelligent Discovery, EarlyView.
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser, Haseeb Muhammad, Junaid Younas, Gillian Lovell, Bianca Migliori, Andreas Dengel, Sheraz Ahmed   +6 more
wiley   +1 more source

Abstract control rules and their use in domain independent planning [PDF]

, 2005
Control rule based planners that take advantage of hand coded domain specific control knowledge currently out-perform fully automatic planners. Generic types have been presented as abstractions of the behaviour of types identified in planning domains and
Murray, Luke Caleb James
core  

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Dynamic Cognition Graph for Adaptive Learning: Integrating Reasoning Evidence and Reinforcement Learning

Applied Sciences
Accurate modeling of learners’ evolving cognitive states is essential for intelligent educational systems, yet many existing knowledge tracing and graph-based approaches rely on static structures or purely sequential representations that inadequately ...
Ying Li, Yiming Gai, Xingyu Wang, Leilei Sun, Xuefei Huang   +4 more
doaj   +1 more source