Results 41 to 50 of about 2,506,651 (240)

Optimal Control Drives Ultrafast and Energy‐Efficient Magnetization Switching in Van der Waals Magnets

Advanced Materials, EarlyView.
ABSTRACT The accelerating expansion of data‐centric technologies is sharply increasing the energy burden of information storage, placing unprecedented pressure on the efficiency of magnetic switching. Conventional field‐driven reversal, once the foundation of magnetic memory, has become impractical in modern architectures due to its high energy cost ...
Mohammad H. Badarneh, PeiYu Cai, Elton J. G. Santos   +2 more
wiley   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Artificial Intelligence Meets Micro/Nanorobotics

Advanced Materials, EarlyView.
Artificial intelligence is transforming micro‐ and nanorobots from externally controlled, task‐specific machines into adaptive, autonomous systems. Machine learning, multimodal perception, digital twins, AI‐guided materials and geometry design enhance propulsion, localization, decision‐making, whichaccelerates clinical and environmental applications ...
Fatma M. Yurtsever, Minitha Cherukutty Ramakrishnan, Ian Kuula Ross, Ivan Zelinka, Hamed Shahsavan, Michal Otyepka, Martin Pumera   +6 more
wiley   +1 more source

Heuristic Algorithm for Conceptual Learning

Engineering Proceedings
This article presents a new heuristic algorithm for conceptual learning. The process of constructing a decision tree through the minimization of a logical function is explained.
Dimitar Minkov, Roumen Trifonov, Dilyana Budakova   +2 more
doaj   +1 more source

An Efficient Building Evacuation Algorithm in Congested Networks

IEEE Access, 2019
This paper proposes a new network model for the building evacuation problem considering congestion levels and provides a mixed integer linear programming (MILP) model and an efficient heuristic algorithm solving the problem.
Chang Hyup Oh, Min Hee Kim, Byung-In Kim, Young Myoung Ko   +3 more
doaj   +1 more source

Inverse Design of Nanoparticulate Materials

Advanced Materials, EarlyView.
Inverse design shifts nanomaterial development from empirical trial‐and‐error to predictive model‐driven strategies. It can rely on knowledge‐based, data‐based, or hybrid process and property functions. This perspective article provides a practical framework for applying inverse design based on instructive examples. It discusses which modeling approach
Nabi Etienne Traoré, Annika Mauch, Michelle Berthold, Lukas Pflug, Wolfgang Peukert, Nicolas Vogel   +5 more
wiley   +1 more source

MOBCA: Multi-Objective Besiege and Conquer Algorithm

Biomimetics
The besiege and conquer algorithm has shown excellent performance in single-objective optimization problems. However, there is no literature on the research of the BCA algorithm on multi-objective optimization problems.
Jianhua Jiang, Jiaqi Wu, Jinmeng Luo, Xi Yang, Zulu Huang   +4 more
doaj   +1 more source

Bitcoin address clustering method based on multiple heuristic conditions

IET Blockchain, 2022
Single heuristic method and incomplete heuristic conditions were difficult to cluster a large number of addresses comprehensively and accurately. Therefore, this paper analysed the associations between Bitcoin transactions and addresses and used six ...
Xi He, Ketai He, Shenwen Lin, Jinglin Yang, Hongliang Mao   +4 more
doaj   +1 more source

Scalable Task Planning via Large Language Models and Structured World Representations

Advanced Robotics Research, EarlyView.
This work efficiently combines graph‐based world representations with the commonsense knowledge in Large Language Models to enhance planning techniques for the large‐scale environments that modern robots will need to face. Planning methods often struggle with computational intractability when solving task‐level problems in large‐scale environments ...
Rodrigo Pérez‐Dattari, Zhaoting Li, Robert Babuška, Jens Kober, Cosimo Della Santina   +4 more
wiley   +1 more source

Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback

Advanced Robotics Research, EarlyView.
BrainBody‐Large Language Model (LLM) introduces a hierarchical, feedback‐driven planning framework where two LLMs coordinate high‐level reasoning and low‐level control for robotic tasks. By grounding decisions in real‐time state feedback, it reduces hallucinations and improves task reliability.
Vineet Bhat, Ali Umut Kaypak, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami   +4 more
wiley   +1 more source