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A generative deep learning framework for airfoil flow field prediction with sparse data

Chinese Journal of Aeronautics 2022年第1期35卷 470-484页

作者： Haizhou WU Xuejun LIU Wei AN Hongqiang LYU MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Aerodynamic Noise Control Mianyang 621000China State Key Laboratory of Aerodynamics Mianyang 621000China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Deep learning has been probed for the airfoil performance prediction in recent years.Compared with the expensive CFD simulations and wind tunnel experiments,deep learning models can be leveraged to somewhat mitigate such expenses with proper means.Nevertheless,effective training of the data-driven models in deep learning severely hinges on the data in diversity and quantity.In this paper,we present a novel data augmented Generative Adversarial Network(GAN),daGAN,for rapid and accurate flow filed prediction,allowing the adaption to the task with sparse data.The presented approach consists of two modules,pre-training module and fine-tuning module.The pre-training module utilizes a conditional GAN(cGAN)to preliminarily estimate the distribution of the training data.In the fine-tuning module,we propose a novel adversarial architecture with two generators one of which fulfils a promising data augmentation operation,so that the complement data is adequately incorporated to boost the generalization of the model.We use numerical simulation data to verify the generalization of daGAN on airfoils and flow conditions with sparse training data.The results show that daGAN is a promising tool for rapid and accurate evaluation of detailed flow field without the requirement for big training data.

关键词： CFD Flow field Generative adversarial networks(GANs) sparse data Supercritical airfoil

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Physics-informed neural network-based petroleum reservoir simulation with sparse data using domain decomposition

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Petroleum Science 2023年第6期20卷 3450-3460页

作者： Jiang-Xia Han Liang Xue Yun-Sheng Wei Ya-Dong Qi Jun-Lei Wang Yue-Tian Liu Yu-Qi Zhang National Key Laboratory of Petroleum Resources and Engineering China University of Petroleum(Beijing)Beijing102249China Department of Oil-Gas Field Development Engineering College of Petroleum EngineeringChina University of PetroleumBeijing102249China PetroChina Research Institute of Petroleum Exploration&Development Beijing100083China Lushang Oilfield Operation Area Jidong Oilfield CompanyPetroChinaTangshan063000HebeiChina

Recent advances in deep learning have expanded new possibilities for fluid flow simulation in petroleum reservoirs.However,the predominant approach in existing research is to train neural networks using high-fidelity numerical simulation data.This presents a significant challenge because the sole source of authentic wellbore production data for training is sparse.In response to this challenge,this work introduces a novel architecture called physics-informed neural network based on domain decomposition(PINN-DD),aiming to effectively utilize the sparse production data of wells for reservoir simulation with large-scale systems.To harness the capabilities of physics-informed neural networks(PINNs)in handling small-scale spatial-temporal domain while addressing the challenges of large-scale systems with sparse labeled data,the computational domain is divided into two distinct sub-domains:the well-containing and the well-free sub-domain.Moreover,the two sub-domains and the interface are rigorously constrained by the governing equations,data matching,and boundary conditions.The accuracy of the proposed method is evaluated on two problems,and its performance is compared against state-of-the-art PINNs through numerical analysis as a benchmark.The results demonstrate the superiority of PINN-DD in handling large-scale reservoir simulation with limited data and show its potential to outperform conventional PINNs in such scenarios.

关键词： Physical-informed neural networks Fluid flow simulation sparse data Domain decomposition

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A Survey of Management of sparse Relational data Sets

A Survey of Management of Sparse Relational Data Sets

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2011年全国电子信息技术与应用学术会议

作者： Jihong YAN 1,Fan XIA 2 1 School of Computer and Information,Shanghai Second Polytechnic University Shanghai 201209 China 2 School of software,East China Normal University,Shanghai 201209 China

This paper is a survey of the current state of the art in management of sparse data sets.It consists of three major parts: the background,the current solutions and a conclusion about those techniques.In the background,some applications of sparse table are presented and the reasons why these tables exist are explained here.In additional,we will give an introduction to the storage formats currently used by these mature DBMS.Then some current available solutions to the handle the wide sparse table in RDBMS will be studied.At last some conclusions will be given.

关键词： sparse data relational wide table

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Factorization Machine Based on Bitwise Feature Importance for CTR Prediction

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国际计算机前沿大会会议论文集 2022年第1期 29-40页

作者： Hao Li Caimao Li Yuquan Hou Hao Lin Qiuhong Chen School of Computer Science and Technology Hainan UniversityHaikou 570228China

Click-through-rate(CTR)prediction is a crucial task in recommendation systems.The accuracy of CTR prediction is strongly influenced by the precise extraction of essential data and the modeling strategy chosen.The data of the CTR task are often very sparse,and Factorization Machines(FMs)are a class of general predictors working effectively with it.However,the performance of FMs can be limited by the fixed feature representation and the same weight of different features.In this work,we propose an improved Bitwise Feature Importance Factorization Machine(BFIFM)to improve the accuracy.The necessity of learning the degree of effect of the same feature under various situations is learned through the low-order intersection method,and the deep neural network(DNN)in our model is used in parallel to study high-order intersections.According to the final results obtained,the BFIFM model significantly outperforms other state-of-the-art models.

关键词： Factorization machines Deep learning Recommendation sparse data

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