Results 241 to 250 of about 11,984,557 (361)
Hybrid materials enable high‐performance components but are challenging to process. This study explores an inductive heating concept with spray cooling for steel–aluminum specimens in a two‐step process including friction welding and cup backward extrusion.
Armin Piwek+7 more
wiley +1 more source
Accelerating RRT* convergence with novel nonuniform and uniform sampling approach. [PDF]
Ganesan S+4 more
europepmc +1 more source
Algorithm 135: Crout with equilibration and iteration [PDF]
W. M. McKeeman
openalex +1 more source
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+4 more
wiley +1 more source
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
Optimization of multi-structural parameters in metamaterials based on the DGN co-simulation method. [PDF]
Jin S, Chen F, Bai J, Liu B.
europepmc +1 more source
Algorithm 288: solution of simultaneous linear Diophantine equations [F4] [PDF]
W. A. Blankinship
openalex +1 more source
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
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+4 more
wiley +1 more source
PDCSA: A parallel discrete crow search algorithm for influence maximization in social networks. [PDF]
Han L, Yang K, Ming Y, Tang J.
europepmc +1 more source