Results 131 to 140 of about 13,735 (297)

Automatic Determination of Quasicrystalline Patterns from Microscopy Images

Advanced Intelligent Discovery, EarlyView.
This work introduces a user‐friendly machine learning tool to automatically extract and visualize quasicrystalline tiling patterns from atomically resolved microscopy images. It uses feature clustering, nearest‐neighbor analysis, and support vector machines. The method is broadly applicable to various quasicrystalline systems and is released as part of
Tano Kim Kender, Marco Corrias, Cesare Franchini   +2 more
wiley   +1 more source

A load forecasting method for IESs using TimeVAE and transfer learning

Zhejiang dianli
In this paper， an integrated learning approach based on time variational autoencoder （TimeVAE） and transfer learning （TL） is proposed to address the accuracy degradation in integrated energy systems （IESs） load forecasting caused by insufficient ...
CHEN Zhe, ZHOU Jinhui, JIN Donghui, CHEN Jiguang, MA Hengrui, ZHANG Jiaxin, ZHU Suxun   +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

Latent space improved masked reconstruction model for human skeleton-based action recognition

Frontiers in Neurorobotics
Human skeleton-based action recognition is an important task in the field of computer vision. In recent years, masked autoencoder (MAE) has been used in various fields due to its powerful self-supervised learning ability and has achieved good results in ...
Enqing Chen, Xueting Wang, Xin Guo, Ying Zhu, Dong Li   +4 more
doaj   +1 more source

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

Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model

npj Computational Materials
We introduce a deep Kernel learning variational autoencoder (DKL-VAE) framework that integrates the generative power of a variational autoencoder (VAE) with the predictive nature of deep Kernel learning (DKL).
Boris N. Slautin, Utkarsh Pratiush, Doru C. Lupascu, Maxim A. Ziatdinov, Sergei V. Kalinin   +4 more
doaj   +1 more source

Disentangling Disentanglement in Variational Autoencoders

, 2018
We develop a generalisation of disentanglement in VAEs---decomposition of the latent representation---characterising it as the fulfilment of two factors: a) the latent encodings of the data having an appropriate level of overlap, and b) the aggregate encoding of the data conforming to a desired structure, represented through the prior.
Mathieu, E, Rainforth, T, Siddharth, N, Teh, Y   +3 more
openaire   +4 more sources

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

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

A Variational Autoencoder-Based Dimensionality Reduction Technique for Generation Forecasting in Cyber-Physical Smart Grids [PDF]

, 2021
A Variational Autoencoder-Based Dimensionality Reduction Technique for Generation Forecasting in Cyber-Physical Smart ...
MA Mahmud (9864944), Shama Islam (13094109), Devinder Kaur (13066719)   +2 more
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