Results 251 to 260 of about 385,356 (336)

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

Core solidification and dynamo evolution in a mantle‐stripped planetesimal

, 2016
A. Scheinberg, L. Elkins‐Tanton, Gerald Schubert, D. Bercovici   +3 more
semanticscholar   +1 more source

Endogenous-Exogenous Analyses of the Solidification Structure in 475 mm Extra-Thick Slabs: Columnar-to-Equiaxed Positioning and Effect of Strand Electromagnetic Stirring. [PDF]

Materials (Basel)
Yu K, Xu L, Zhang Y, Zhang H, Zhan Z.
europepmc   +1 more source

Numerical Modelling of Pure Metal Solidification using OpenFOAM

, 2019
Singh, Anup, Kumar Alok, Kumar, Arvind
openalex   +1 more source

Machine Learning Applied to High Entropy Alloys under Irradiation

Advanced Engineering Materials, EarlyView.
Designing alloys for extreme environments demands fast, trustworthy prediction. This review charts how machine learning—especially machine‐learned interatomic potentials and predictive models based on experiment‐informed datasets—captures the complexity of high‐entropy alloys in extreme environments, predicts phase formation, mechanical properties, and
Amin Esfandiarpour, Sri Tapaswi Nori, Silvia Bonfanti, Mikko Alava, Antoni Wadowski, Wenyi Huo, Łukasz Kurpaska, Michał Pecelerowicz, Jan S. Wróbel   +8 more
wiley   +1 more source

Time-Resolved In Situ Measurements During Rapid Alloy Solidification: Experimental Insight for Additive Manufacturing

, 2016
J. McKeown, K. Zweiacker, Can Liu, Daniel R. Coughlin, Amy J. Clarke, J. Kevin Baldwin, John W. Gibbs, J. Roehling, S. Imhoff, P. Gibbs, D. Tourret, Jörg M. K. Wiezorek, Geoffrey H. Campbell   +12 more
semanticscholar   +1 more source

Study on the Corrosion Behavior of Additively Manufactured NiCoCrFe_yMo_x High-Entropy Alloys in Chloride Environments. [PDF]

Materials (Basel)
Xie C, Hou Y, Song Y, Mi Z, Li F, Guo W, He D.   +6 more
europepmc   +1 more source

The Impact of Social Class Solidification on the Educational Opportunities of the Next Generation in the Philippines and the Barriers to Social Mobility in Primary Education

Marisol C. Muñoz-Carrasco
openalex   +1 more source

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