Machine learning for active matter
The availability of large datasets has boosted the application of machine learning in many
fields and is now starting to shape active-matter research as well. Machine learning …
fields and is now starting to shape active-matter research as well. Machine learning …
[HTML][HTML] Neural agent-based production planning and control: An architectural review
Nowadays, production planning and control must cope with mass customization, increased
fluctuations in demand, and high competition pressures. Despite prevailing market risks …
fluctuations in demand, and high competition pressures. Despite prevailing market risks …
Quantitative digital microscopy with deep learning
Video microscopy has a long history of providing insight and breakthroughs for a broad
range of disciplines, from physics to biology. Image analysis to extract quantitative …
range of disciplines, from physics to biology. Image analysis to extract quantitative …
Free vibration response of auxetic honeycomb sandwich plates using an improved higher-order ES-MITC3 element and artificial neural network
This article presents the combination of mixed interpolation of tensorial components
technique of triangular elements and the edge-based smoothed finite element method (ES …
technique of triangular elements and the edge-based smoothed finite element method (ES …
Towards sustainable use of foundry by-products: Evaluating the compressive strength of green concrete containing waste foundry sand using hybrid biogeography …
R Kazemi, MZ Naser - Journal of Building Engineering, 2023 - Elsevier
Waste foundry sand (WFS) is known as the main waste material of foundry industries, and its
disposal cost and environmental threats have become one of the major challenges in many …
disposal cost and environmental threats have become one of the major challenges in many …
[HTML][HTML] A multiscale deep learning model for elastic properties of woven composites
Time-consuming and costly computational analysis expresses the need for new methods for
generalizing multiscale analysis of composite materials. Combining neural networks and …
generalizing multiscale analysis of composite materials. Combining neural networks and …
Evaluating the rapid chloride permeability of self-compacting concrete containing fly ash and silica fume exposed to different temperatures: An artificial intelligence …
R Kazemi, A Gholampour - Construction and Building Materials, 2023 - Elsevier
One of the major known challenges of improving durability of concrete structures is reducing
the permeability of concrete to retard the transport of chloride ions. In this regard, the …
the permeability of concrete to retard the transport of chloride ions. In this regard, the …
[HTML][HTML] Recurrent neural networks and transfer learning for predicting elasto-plasticity in woven composites
Woven composites exhibit complex meso-scale behavior depending on meso-and micro-
structural parameters. Accurately modeling their mechanical response is challenging and …
structural parameters. Accurately modeling their mechanical response is challenging and …
Prediction of IC engine performance and emission parameters using machine learning: A review
K Karunamurthy, AA Janvekar, PL Palaniappan… - Journal of Thermal …, 2023 - Springer
The human kind is facing various natural calamities such as Elnino, forest fires, climate
change, etc., due to environmental degradation and pollution. The United Nations has come …
change, etc., due to environmental degradation and pollution. The United Nations has come …
[HTML][HTML] Nonlocal free vibration of functionally graded porous nanoplates using higher-order isogeometric analysis and ANN prediction
The main objective of this article is to propose a new shear continuous function used for the
free vibration analysis of functionally graded (FG) nanoplates with the presence of internal …
free vibration analysis of functionally graded (FG) nanoplates with the presence of internal …