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Geometric deep learning: progress, applications and challenges

Science China(Information Sciences) 2022年第2期65卷 238-240页

作者： Wenming CAO Canta ZHENG Zhiyue YAN Weixin XIE College of Electronics and Information Engineering Shenzhen University

deep learning technology,like convolutional neural networks,has been widely used in machine learning applications such as image detection and classification,speech processing,natural language processing and cross-modal retrieval[1].Although deep learning technology has achieved great success in various fields,it has been unable to match non-Euclidean data effectively.In this context,

关键词： Geometric deep learning manifold deep learning CNN Graph convolutional neural networks high-dimensional information

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Semantic segmentation of pyramidal neuron skeletons using Geometric deep learning

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Journal of Innovative Optical Health Sciences 2023年第6期16卷 69-76页

作者： Lanlan Li Jing Qi Yi Geng Jingpeng Wu Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University FuzhouFujian 350116P.R.China Center for Computational Neuroscience Flatiron Institute New York 10010USA

Neurons can be abstractly represented as skeletons due to the filament nature of neurites.With the rapid development of imaging and image analysis techniques,an increasing amount of neuron skeleton data is being produced.In some scienti fic studies,it is necessary to dissect the axons and dendrites,which is typically done manually and is both tedious and time-consuming.To automate this process,we have developed a method that relies solely on neuronal skeletons using Geometric deep learning(GDL).We demonstrate the effectiveness of this method using pyramidal neurons in mammalian brains,and the results are promising for its application in neuroscience studies.

关键词： Pyramidal neuron Geometric deep learning neuron skeleton semantic segmentation point cloud.

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Graph Neural Architecture Search:A Survey

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Tsinghua Science and Technology 2022年第4期27卷 692-708页

作者： Babatounde Moctard Oloulade Jianliang Gao Jiamin Chen Tengfei Lyu Raeed Al-Sabri School of Computer Science and Engineering Central South UniversityChangsha 410083China

In academia and industries,graph neural networks(GNNs)have emerged as a powerful approach to graph data processing ranging from node classification and link prediction tasks to graph clustering tasks.GNN models are usually handcrafted.However,building handcrafted GNN models is difficult and requires expert experience because GNN model components are complex and sensitive to variations.The complexity of GNN model components has brought significant challenges to the existing efficiencies of GNNs.Hence,many studies have focused on building automated machine learning frameworks to search for the best GNN models for targeted tasks.In this work,we provide a comprehensive review of automatic GNN model building frameworks to summarize the status of the field to facilitate future progress.We categorize the components of automatic GNN model building frameworks into three dimensions according to the challenges of building them.After reviewing the representative works for each dimension,we discuss promising future research directions in this rapidly growing field.

关键词： graph neural network neural architecture search automated machine learning Geometric deep learning

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A Survey of deep learning-Based Mesh Processing

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Communications in Mathematics and Statistics 2022年第1期10卷 163-194页

作者： He Wang Juyong Zhang School of Mathematical Sciences University of Science and Technology of ChinaHefei 230026People’s Republic of China

In the past ten years,deep learning technology has achieved a great success in many fields,like computer vision and speech recognition.Recently,large-scale geometry data become more and more available,and the learned geometry priors have been successfully applied to 3D computer vision and computer graphics fields.Different from the regular representation of images,surface meshes have irregular structures with different vertex numbers and topologies.Therefore,the traditional convolution neural networks used for images cannot be directly used to handle surface meshes,and thus,many methods have been proposed to solve this problem.In this paper,we provide a comprehensive survey of existing Geometric deep learning methods formesh processing.We first introduce the relevant knowledge and theoretical background of Geometric deep learning and some basic mesh data knowledge,including some commonly used mesh datasets.Then,we review various deep learning models for mesh data with two different types:graph-based methods and mesh structure-based methods.We also review the deep learning-based applications for mesh data.In the final,we give some potential research directions in this field.

关键词： Geometric deep learning Non-Euclidean space Mesh Convolution Spectral domain Spatial domain

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deep convolutional surrogates and freedom in thermal design

Energy and AI

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Energy and AI 2023年第3期13卷 126-136页

作者： Hadi Keramati Feridun Hamdullahpur Department of Mechanical and Mechatronics Engineering University of Waterloo200 University AvenueWest WaterlooOntarioN2L 3G1Canada

A deep learning approach is presented for heat transfer and pressure drop prediction of complex fin geometries generated using composite Bézier curves.Thermal design process includes iterative high fidelity simulation which is complex,computationally expensive,and time-consuming.With the advancement in machine learning algorithms as well as Graphics Processing Units(GPUs),parallel processing architecture of GPUs can be used to accelerate thermo-fluid simulation.In this study,Convolutional Neural Networks(CNNs)are used to predict results of Computational Fluid Dynamics(CFD)directly from topologies saved as images.A design space with a single fin as well as multiple morphable fins are studied.A comparison of Xception network and regular CNN is presented for the case with a single fin design.Results show that high accuracy in prediction is observed for single fin design particularly using Xception network.Xception network provides 98 percent accuracy in heat transfer and pressure drop prediction of the single fin design.Increasing the design freedom to multiple fins increases the error in prediction.This error,however,remains within three percent of the ground truth values which is valuable for design purpose.The presented predictive model can be used for innovative BREP-based fin design optimization in compact and high efficiency heat exchangers.

关键词： Geometric deep learning Geometry processing Heat exchanger Design freedom Surrogate modeling

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