Results 191 to 200 of about 1,101,099 (365)

Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics

Advanced Engineering Materials, EarlyView.
Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani, Aditi Singh, Behnam Ashrafi   +2 more
wiley   +1 more source

Static and Dynamic Behavior of Novel Y‐Shaped Sandwich Beams Subjected to Compressive Loadings: Integration of Supervised Learning and Experimentation

Advanced Engineering Materials, EarlyView.
In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi, Saeed Kamarian, Mahdi Bodaghi, Saeed Saber‐Samandari, Jung‐il Song   +4 more
wiley   +1 more source

Incorporating query-specific feedback into learning-to-rank models

, 2014
Ethem F. Can, W. Bruce Croft, R. Manmatha   +2 more
openalex   +2 more sources

Consolidate Overview of Ribonucleic Acid Molecular Dynamics: From Molecular Movements to Material Innovations

Advanced Engineering Materials, EarlyView.
Molecular dynamics simulations are advancing the study of ribonucleic acid (RNA) and RNA‐conjugated molecules. These developments include improvements in force fields, long‐timescale dynamics, and coarse‐grained models, addressing limitations and refining methods.
Kanchan Yadav, Iksoo Jang, Jong Bum Lee
wiley   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Advanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source

Smart learning: A search-based approach to rank change and defect prone classes

, 2015
Carol V. Alexandru, Annibale Panichella, Sebastiano Panichella, Alberto Bacchelli, Harald C. Gall   +4 more
openalex   +1 more source

Quad-Networks: Unsupervised Learning to Rank for Interest Point Detection [PDF]

, 2017
Nikolay Savinov, Akihito Seki, L’ubor Ladický, Torsten Sattler, Marc Pollefeys   +4 more
openalex   +1 more source

Multiscale Modeling of Process‐Induced Defects in Fused Filament Fabrication‐Printed Materials

Advanced Engineering Materials, EarlyView.
This study presents a predictive multiscale modeling tool for defect analysis of fused filament fabricated‐printed materials and their performance prediction using a mechanistic data science‐based reduced‐order modeling approach. Process‐induced defects are inherent to additively manufactured parts and significantly influence the performance of printed
Satyajit Mojumder, Derick Suarez, Trevor Abbott, Wing Kam Liu   +3 more
wiley   +1 more source

Incorporating Radiologist Knowledge Into MRI Quality Metrics for Machine Learning Using Rank-Based Ratings. [PDF]

J Magn Reson Imaging
Tang C, Eisenmenger LB, Rivera-Rivera L, Huo E, Junn JC, Kuner AD, Oechtering TH, Peret A, Starekova J, Johnson KM.   +9 more
europepmc   +1 more source

Bayesian Learning for Low-Rank matrix reconstruction

, 2015
Martin Sundin, Cristian R. Rojas, Magnus Jansson, Saikat Chatterjee   +3 more
openalex   +2 more sources