Results 121 to 130 of about 39,632 (294)

LoGAN: Local generative adversarial network for novel structure prediction

The efficient generation and filtering of candidate structures for new materials is becoming increasingly important as starting points for computational studies.
Esther, Heid, Georg K. H., Madsen, Jasper, De Landsheere, Péter, Kovács   +3 more
core   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

A Solution for Exosome‐Based Analysis: Surface‐Enhanced Raman Spectroscopy and Artificial Intelligence

Advanced Intelligent Discovery, EarlyView.
Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz, Zakarya Al‐Shaebi, Omer Aydin   +2 more
wiley   +1 more source

Image Text Deblurring Method Based on Generative Adversarial Network

, 2020
In the automatic sorting process of express delivery, a three-segment code is used to represent a specific area assigned by a specific delivery person. In the process of obtaining the courier order information, the camera is affected by factors such as ...
Chunxue Wu, Haiyan Du, Qunhui Wu, Sheng Zhang   +3 more
core   +1 more source

A review of generative adversarial network applications in optical coherence tomography image analysis

, 2022
Optical coherence tomography (OCT) has revolutionized ophthalmic clinical practice and research, as a result of the high-resolution images that the method is able to capture in a fast, non-invasive manner.
Kugelman, Jason, Alonso-Caneiro, D, Read, Scott A., Alonso-Caneiro, David, Read, SA, Kugelman, J, Collins, Michael J., Collins, MJ   +7 more
core   +1 more source

Application of Neural Networks for Advanced Ir Spectroscopy Characterization of Ceria Catalysts Surfaces

Advanced Intelligent Discovery, EarlyView.
A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali, Lachlan Caulfield, Eric Sauter, Alexei Nefedov, Chengwu Yang, Christof Wöll   +5 more
wiley   +1 more source

Multi-class data augmentation for prediction of postpartum hemorrhage using improved ACGAN

Alexandria Engineering Journal
The dataset of primary postpartum hemorrhage (PPH) faces the challenge of insufficient samples, and Generative Adversarial Networks (GANs) have shown considerable promise in addressing the scarcity and imbalance of samples in the diagnosis of PPH ...
Xiaodan Li, Yue Zhou, Fengchun Gao, Di Cheng, Wushan Li, Kaijian Xia, Hongsheng Yin   +6 more
doaj   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Generative Adversarial Networks in Speech Enhancement: A Survey

IEEE Access
Generative adversarial networks are a powerful type of model in deep learning. They have been successfully applied within different domains. This review focuses on the usage of generative adversarial networks for speech enhancement.
Justina Ramonaite, Grazina Korvel, Gintautas Tamulevicius   +2 more
doaj   +1 more source

On the "steerability" of generative adversarial networks

CoRR, 2019
An open secret in contemporary machine learning is that many models work beautifully on standard benchmarks but fail to generalize outside the lab. This has been attributed to biased training data, which provide poor coverage over real world events.
Ali Jahanian 0002, Lucy Chai, Phillip Isola   +2 more
openaire   +3 more sources