Results 81 to 90 of about 1,025,786 (272)

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

open access: yesAdvanced 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   +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

open access: yesAdvanced 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   +4 more
wiley   +1 more source

The Java Learning Machine: A Learning Management System Dedicated To Computer Science Education

open access: yes, 2011
This paper presents the Java Learning Machine (JLM), a platform dedicated to computer programming education. This generic platform offers support to teachers for creating programming microworlds suitable to teaching courses. It features an integrated and graphical environment, providing a short feedback loop to students in order to improve the ...
Quinson, Martin, Oster, Gérald
openaire   +1 more source

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

open access: yesAdvanced 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

A Web-Based Platform for Predicting Student Careers using Machine Learning with Learning Resources for Computer Science Domains

open access: gold, 2023
Parth Lokhande   +4 more
openalex   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

open access: yesAdvanced 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   +4 more
wiley   +1 more source

X‐Ray Computed Tomography Quantifies Primary Phases and Reveals Crack Morphology in High‐Cycle Fatigue of Aluminum Alloy EN AW‐2618A

open access: yesAdvanced Engineering Materials, EarlyView.
Primary phases and a fatigue crack are studied in a forged blank of an aluminum alloy using synchrotron and laboratory X‐ray computed tomography. To image the crack, the fatigue test is interrupted, and a static tensile load is applied to open the crack.
Jakob Schröder   +6 more
wiley   +1 more source

New Developments in the Field of Production and Application of Multi‐Material Wire Arc Additive Manufacturing Components: A Review

open access: yesAdvanced Engineering Materials, EarlyView.
The utilization of direct energy deposition (DED)‐arc additive manufacturing processes in industrial applications is increasing, and these processes have the potential for multi‐material applications. This work provides a overview of the state of research in DED‐arc made functional graded structures, to establish a link to potential industrial ...
Kai Treutler, Volker Wesling
wiley   +1 more source

Home - About - Disclaimer - Privacy