On-demand prediction of low-frequency average sound absorption coefficient of underwater coating using machine learning

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104163 - 104163

Опубликована: Янв. 1, 2025

Язык: Английский

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Applied Artificial Intelligence in Materials Science and Material Design

Advanced Intelligent Systems, Год журнала: 2025, Номер unknown

Опубликована: Март 2, 2025

Materials science has traditionally relied on a combination of experimental techniques and theoretical modeling to discover develop new materials with desired properties. However, these processes can be time‐consuming, resource‐intensive, often limited by the complexity material systems. The advent artificial intelligence (AI), particularly machine learning, revolutionized offering powerful tools accelerate discovery, design, characterization novel materials. AI not only enhances predictive properties but also streamlines data analysis in like X‐Ray diffraction, Raman spectroscopy, scanning probe microscopy, electron microscopy. By leveraging large datasets, algorithms identify patterns, reduce noise, predict behavior unprecedented accuracy. In this review, recent advancements applications across various domains science, including synchrotron studies, microscopies, metamaterials, atomistic modeling, molecular drug are highlighted. It is discussed how AI‐driven methods reshaping field, making discovery more efficient, paving way for breakthroughs design real‐time analysis.

Язык: Английский

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Architected acoustic metamaterials: An integrated design perspective

Applied Physics Reviews, Год журнала: 2025, Номер 12(1)

Опубликована: Март 1, 2025

The review focuses on architected acoustic metamaterials to manipulate airborne sound waves, with only limited discussions elastic related solid media. We the design of and physical mechanisms underpinning their performance manufacturing methodologies, while also examining potential issues challenges affecting use in acoustics. complexities several metamaterial architectures are discussed. A new classification system is proposed distinguish configurations based typology channels inside meta-atom. Several types architectures, such as perforated micro-perforated panels, foams, resonators, various geometrical paths, piezoelectric patches, fundamental these classes identified commented on. paper describes main measurement techniques used for quantities evaluated, providing a guide characterize assess performance. current designs discussed, focus complex synergy between architectural patterns thickness. clarify distinction metamaterials, emphasizing applications materials that waves fluid offers further comments about need practical tools allow real-world applications.

Язык: Английский

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AInsectID Version 1.1: An Insect Species Identification Software Based on the Transfer Learning of Deep Convolutional Neural Networks

Опубликована: Март 25, 2025

AInsectID Version 1.1 is a Graphical User Interface (GUI)‐operable open‐source insect species identification, color processing, and image analysis software. The software has current database of 150 insects integrates artificial intelligence approaches to streamline the process with focus on addressing prediction challenges posed by mimics. This paper presents methods algorithmic development, coupled rigorous machine training used enable high levels validation accuracy. Our work transfer learning prominent convolutional neural network (CNN) architectures, including VGG16, GoogLeNet, InceptionV3, MobileNetV2, ResNet50, ResNet101. Here, we employ both fine tuning hyperparameter optimization improve performance. After extensive computational experimentation, ResNet101 evidenced as being most effective CNN model, achieving accuracy 99.65%. dataset utilized for sourced from National Museum Scotland, Natural History London, open source datasets Zenodo (CERN's Data Center), ensuring diverse comprehensive collection species.

Язык: Английский

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Hierarchical Design of Mechanical Metamaterials: an Application on Pentamode-like Structures

International Journal of Mechanical Sciences, Год журнала: 2025, Номер unknown, С. 110232 - 110232

Опубликована: Апрель 1, 2025

Язык: Английский

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Inverse design of non-parametric acoustic metamaterials via transfer-learned dual variational autoencoder with latent space-based data augmentation

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 151, С. 110735 - 110735

Опубликована: Апрель 8, 2025

Язык: Английский

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Quantum metamaterials: Applications in quantum information science

Deleted Journal, Год журнала: 2025, Номер 2(2)

Опубликована: Апрель 9, 2025

Metamaterials are a class of artificially engineered materials with periodic structures possessing exceptional properties not found in conventional materials. This definition can be extended when we introduce degree freedom by adding quantum elements such as dots, cold atoms, Josephson junctions, and molecules, making metamaterials highly valuable for various applications. have been used to achieve invisibility cloaking, super-resolution, energy harvesting, sensing, among other Most these applications performed the classical regime. gradually made their way into regime since advent computing sensing imaging. Quantum relatively new technology, use information processing has proliferated. We restrict this study state manipulation control, entanglement, single photon generation, switching, engineering, key distribution, algorithms, orbital angular momentum, Considering developments, examine theory, fabrication, contributing how contribute field. find that ability harness unique drive is great importance, they potential unlock possibilities revolutionizing processing, bringing world closer practical technologies unprecedented capabilities. conclude suggesting possible future research directions.

Язык: Английский

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Recent Innovations in EMI Shielding Materials for Stealth Technology

Synthetic Metals, Год журнала: 2025, Номер unknown, С. 117874 - 117874

Опубликована: Апрель 1, 2025

Язык: Английский

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An automated design framework for composite mechanical metamaterials and its application to 2D pentamode materials

International Journal of Mechanical Sciences, Год журнала: 2024, Номер 276, С. 109393 - 109393

Опубликована: Май 16, 2024

Until relatively recently most mechanical metamaterial classes being studied have been composed of a single solid constituent phase and design has focused almost exclusively on structural geometry. Additional dimensions can be introduced by accepting heterogeneity varying materiality, i.e., allowing properties to vary across the metamaterial's unit cells or even from cell in domain, creating composite metamaterials. This higher dimensionality significantly expands effective property envelope, but additional complexity also presents significant hurdle. To overcome challenge, an automated framework is proposed that leverages modern evolutionary computation techniques, combined with finite element analysis for fitness evaluation, discretized voxelated domain. However, this approach introduces stochastic statistical aspects process, which requires processing successfully extract useful solutions. A case study presented used generate 2D structures exhibit pentamode-like behavior. Pentamode metamaterials, are best known extreme bulk-to-shear modulus ratios (B/G), offer unique control over elastic make particularly interesting test case. The objective was defined as maximizing B/G square It found process converges solution rapidly, generally less hundred generations. ratio values 10,000 more were obtained, largely exceeding those commonly literature experimental pentamode These generated designs feature reduced stress concentrations due elimination point-like connections between lattice struts, addresses key practical limitation diamond pentamodes. observed whatever initial variety moduli voxels evolution progressed collapsed much smaller number, often binary very stiff limited number softer at locations acted hinges.

Язык: Английский

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Recent Innovations in Microstrip Patch Antennas: Biomedical Uses and Wireless Integration

Deleted Journal, Год журнала: 2025, Номер unknown

Опубликована: Фев. 21, 2025

Язык: Английский

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