Results 101 to 110 of about 1,606,312 (290)
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
Socially Aware Heterogeneous Wireless Networks
Sensors, 2015 The development of smart cities has been the epicentre of many researchers’ efforts during the past decade. One of the key requirements for smart city networks is mobility and this is the reason stable, reliable and high-quality wireless communications ...
Pavlos Kosmides +5 more
doaj +1 more source
On regularization algorithms in learning theory
Journal of Complexity, 2007 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Frank Bauer +2 more
openaire +3 more sources
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
Advanced Engineering Materials, EarlyView.This study presents an infrared monitoring approach for direct laser interference patterning (DLIP) combined with a convolutional neural network (CNN). Thermal emission data captured during structuring are used to predict surface topography parameters.
Lukas Olawsky +5 more
wiley +1 more source
Research on SNN Learning Algorithms and Networks Based on Biological Plausibility
IEEE AccessSpiking Neural Networks, inspired by the brain’s neuronal information processing mech- anisms, utilize sparse, event-based spike signals to emulate biological computation.
Bingqiang Huo +5 more
doaj +1 more source
Learning cloth manipulation with demonstrations [PDF]
, 2019 Recent advances in Deep Reinforcement learning and computational capabilities of GPUs have led to variety of research being conducted in the learning side of robotics.
Alenyà Ribas, Guillem +2 more
core
Numerical Modeling of Tank Cars Carrying Hazardous Materials With and Without Composite Metal Foam
Advanced Engineering Materials, EarlyView.Large‐scale puncture models consisting of hazardous materials (HAZMATs) tank car with protective steel–steel composite metal foam (S–S CMF) are solved numerically. Tank car plate with added 10.91–13.33 mm thick S–S CMF layer does not puncture. Protective S–S CMF absorbs impact energy, reduces plate deformation, and prevents shear bands formation ...
Aman Kaushik, Afsaneh Rabiei
wiley +1 more source
, 2020 Os modelos de aprendizagem automática dependem dos dados para aprender qualquer tarefa e, dependendo da diversidade de cada um dos elementos da tarefa e dos objetivos do projeto, a quantidade de dados pode ser elevada, o que, por sua vez, pode aumentar exponencialmente o tempo de aprendizagem e o custo computacional.
openaire +1 more source
Advanced Engineering Materials, EarlyView.Additive manufacturing provides precise control over the placement of continuous fibres within polymer matrices, enabling customised mechanical performance in composite components. This article explores processing strategies, mechanical testing, and modelling approaches for additive manufactured continuous fibre‐reinforced composites.
Cherian Thomas, Amir Hosein Sakhaei
wiley +1 more source