3 citations · 3 across the 5 of their papers we have counts for
5 papers
Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming
Yingxue Zhao, Qianyi Chen, Haoran Li +5
In recent years, various artificial intelligence-based surrogate models have been proposed to provide rapid manufacturability predictions of material forming processes. However, tr…
Contrastive Learning for Efficient Transaction Validation in UTXO-based Blockchains
Hamid Attar, Luigi Lunardon, Alessio Pagani
This paper introduces a Machine Learning (ML) approach for scalability of UTXO-based blockchains, such as Bitcoin. Prior approaches to UTXO set sharding struggle with distributing…
A review of graph neural network applications in mechanics-related domains
Yingxue Zhao, Haoran Li, Haosu Zhou +3
Mechanics-related problems often present unique challenges in achieving accurate geometric and physical representations, particularly for non-uniform structures. Graph neural netwo…
Development of a deep learning platform for optimising sheet stamping geometries subject to manufacturing constraints
Hamid Reza Attar, Alistair Foster, Nan Li
The latest sheet stamping processes enable efficient manufacturing of complex shape structural components that have high stiffness to weight ratios, but these processes can introdu…
Rapid feasibility assessment of components formed through hot stamping: A deep learning approach
Hamid Reza Attar, Haosu Zhou, Alistair Foster +1
The novel non-isothermal Hot Forming and cold die Quenching (HFQ) process can enable the cost-effective production of complex shaped, high strength aluminium alloy panel components…