Results 121 to 130 of about 134,465 (305)

Conditional noise generative adversarial networks with Siamese neural network for longer time series forecasting

Scientific Reports
Generative adversarial networks have achieved strong results in computer vision, but their use in time series forecasting remains limited. This paper proposes a conditional noise generative adversarial network with a Siamese neural network as ...
Haotian Mao, Xiao Feng
doaj   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Research on intrusion detection for maritime meteorological sensor network based on balancing generative adversarial network

Tongxin xuebao, 2023
Aiming at the problem that the resources of maritime mobile terminals were limited and the network traffic was imbalanced in the MMSN (maritime meteorological sensor network) environment, which made it difficult to detect network intrusion accurately, a ...
Xin SUN, Guifu ZHANG, Hongyan XING, Wang Zenghui   +3 more
doaj  

Multiple Generative Adversarial Networks Analysis for Predicting Photographers' Retouching [PDF]

, 2020
Marc Bickel, Samuel Dubuis, Sébastien Gachoud   +2 more
openalex   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks [PDF]

, 2018
Emilien Dupont, Tuanfeng Zhang, Peter Tilke, Liang Lin, William J. Bailey   +4 more
openalex   +1 more source

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

Advanced Intelligent Discovery, EarlyView.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang, Min Zhu, Wei Xiong, Si‐Wei Liu, Lu Lu   +4 more
wiley   +1 more source

SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color [PDF]

, 2019
Youngjoo Jo, Jongyoul Park
openalex   +1 more source

Simulation of NDVI imagery using Generative Adversarial Network and Sentinel-1 C-SAR data

, 2022
Rei Sonobe, Haruyuki Seki, Hideki SHIMAMURA, Kan-ichiro MOCHIZUKI, Genya SAITO, Kunihiko YOSHINO   +5 more
openalex   +2 more sources

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source