Results 61 to 70 of about 7,724,255 (252)
What Do Large Language Models Know About Materials?
Advanced Engineering Materials, EarlyView.If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer +2 more
wiley +1 more source
A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions
Advanced Engineering Materials, EarlyView.This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice +2 more
wiley +1 more source
Higher Education: Increased and Equitable Access to Quality Learning Opportunities
, 2019 Given that many developing countries seek to increase participation in Higher Education (HE), COL will work with Ministries of Education and HE Institutions to support capacity building including the development and implementation of Open and Distance ...
Commonwealth of Learning
core +1 more source
Science and Technology for Energy TransitionServerless computing has evolved as a prominent paradigm within cloud computing, providing on-demand resource provisioning and capabilities crucial to Science and Technology for Energy Transition (STET) applications.
Kaur Jasmine, Chana Inderveer, Bala Anju
doaj +1 more source
Combining Q-Learning and the Hungarian Algorithm for Multi-Robot Systems in Dynamic Environments [PDF]
E3S Web of ConferencesThis paper presents a hierarchical reinforcement learning framework for multi-robot systems in dynamic warehouse environments. The proposed approach integrates Q-learning at two levels: motion control and task reassignment.
Nguyen Hoang Mai +4 more
doaj +1 more source
, 2021 The Q-learning algorithm is known to be affected by the maximization bias, i.e. the systematic overestimation of action values, an important issue that has recently received renewed attention. Double Q-learning has been proposed as an efficient algorithm
Zhu, Rong, Rigotti, Mattia
core +1 more source
Advanced Engineering Materials, EarlyView.Geometry‐driven thermal behavior in wire‐arc additive manufacturing (WAAM) influences microstructural evolution during nonequilibrium solidification of a chemically complex Fe–Cr–Nb–W–Mo–C nanocomposite system. By comparing different deposits configurations, distinct entropy–cooling rate correlations, segregation, and carbide evolution are revealed ...
Blanca Palacios +5 more
wiley +1 more source
Complex System Modeling and SimulationThe lot-streaming flowshop scheduling problem with equal-size sublots (ELFSP) is a significant extension of the classic flowshop scheduling problem, focusing on optimize makespan.
Ping Wang, Renato De Leone, Hongyan Sang
doaj +1 more source
A Reinforcement Learning Approach to Determine When and How Many Stocks to Buy in Stock Trading [PDF]
پژوهشهای نظری و کاربردی هوش ماشینیDue to the volatility and uncertainty inherent in the stock market, devising an optimal trading strategy is a complex endeavor. Given the non-repetitive nature of trading circumstances, learning through interactions becomes imperative.
ولی درهمی, Fatemeh Darezereshki
doaj +1 more source
Advanced Engineering Materials, EarlyView.A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares +3 more
wiley +1 more source