Results 111 to 120 of about 134,465 (305)

Inverse Design of Alloys via Generative Algorithms: Optimization and Diffusion within Learned Latent Space

Advanced Intelligent Discovery, EarlyView.
This work presents a novel generative artificial intelligence (AI) framework for inverse alloy design through operations (optimization and diffusion) within learned compact latent space from variational autoencoder (VAE). The proposed work addresses challenges of limited data, nonuniqueness solutions, and high‐dimensional spaces.
Mohammad Abu‐Mualla, Ellis Crabtree, Fredrick Michael, Yayue Pan, Jida Huang   +4 more
wiley   +1 more source

Regional attention generative adversarial network

Electronics Letters, 2019
In this Letter, the authors propose a novel attention mechanism combined with a classical generative adversarial network (GAN) model to improve the visual quality of generated samples. This novel attention model is named regional attention GAN.
Wei Wang, Haifeng Hu, Yi Huang, Chongchong Ruan, Dihu Chen   +4 more
doaj   +1 more source

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

A Deep Learning-Based Two-Branch Generative Adversarial Network for Image De-Raining

Sensors
Raindrops can scatter and absorb light, causing images to become blurry or distorted. To improve image quality by reducing the impact of raindrops, this paper proposes a novel generative adversarial network for image de-raining. The network comprises two
Liquan Zhao, Jie Long, Tie Zhong
doaj   +1 more source

Hamiltonian quantum generative adversarial networks

Physical Review Research
We propose Hamiltonian quantum generative adversarial networks (HQuGANs) to learn to generate unknown input quantum states using two competing quantum optimal controls. The game-theoretic framework of the algorithm is inspired by the success of classical generative adversarial networks in learning high-dimensional distributions.
Leeseok Kim, Seth Lloyd, Milad Marvian
openaire   +3 more sources

Image generations techniques using Generative adversarial networks

Adaptivni Sistemi Avtomatičnogo Upravlinnâ, 2023
The object of research is image generation algorithms based on GAN. The article reviews the main uses of these networks for image generation and main types of such algorithms, which can be used for this. Generative Adversarial Networks (GANs) have been a significant breakthrough in machine learning, allowing the generation of images that are ...
Ivanov, A., Onyshchenko, V.
openaire   +2 more sources

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics

Energy and AI
Lithium batteries find extensive applications in energy storage. Temperature is a crucial indicator for assessing the state of lithium-ion batteries, and numerous experiments require thermal images of lithium-ion batteries for research purposes. However,
Fengshuo Hu, Chaoyu Dong, Luyu Tian, Yunfei Mu, Xiaodan Yu, Hongjie Jia   +5 more
doaj   +1 more source

Accelerated Solutions of Coupled Phase-Field Problems using Generative Adversarial Networks [PDF]

, 2022
Vir Karan, A. Maruthi Indresh, Saswata Bhattacharyya   +2 more
openalex   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source