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| 1 | AN ANISOTROPIC NONCONFORMING FINITE ELEMENT WITH SOME SUPERCONVERGENCE RESULTS显示文摘The main aim of this paper is to study the error estimates of a nonconforming finite element with some superconvergence results under anisotropic meshes. The anisotropic interpolation error and consistency error estimates are obtained by using some novel approaches and techniques, respectively. Furthermore, the superclose and a superconvergence estimate on the central points of elements are also obtained without the regularity assumption and quasi-uniform assumption requirement on the meshes. Finally, a numerical test is carried out, which coincides with our theoretical analysis. | Dong-yangShi Shi-pengMao Shao-chunChen | 2005 | Journal of Computational Mathematics2005，23，3： | 184 |
| 2 | A general-purpose machine learning framework for predicting properties of inorganic materials显示文摘A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data.While prior examples have demonstrated successful models for some applications,many more applications exist where machine learning can make a strong impact.To enable faster development of machine-learning-based models for such applications,we have created a framework capable of being applied to a broad range of materials data.Our method works by using a chemically diverse list of attributes,which we demonstrate are suitable for describing a wide variety of properties,and a novel method for partitioning the data set into groups of similar materials to boost the predictive accuracy.In this manuscript,we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials,such as band gap energy and glass-forming ability. | Logan Ward Ankit Agrawal Alok Choudhary Christopher Wolverton | 2016 | npj Computational Materials2016，，1： | 83 |
| 3 | Artificial Neural Network Methods for the Solution of Second Order Boundary Value Problems显示文摘We present a method for solving partial differential equations using artificial neural networks and an adaptive collocation strategy.In this procedure,a coarse grid of training points is used at the initial training stages,while more points are added at later stages based on the value of the residual at a larger set of evaluation points.This method increases the robustness of the neural network approximation and can result in significant computational savings,particularly when the solution is non-smooth.Numerical results are presented for benchmark problems for scalar-valued PDEs,namely Poisson and Helmholtz equations,as well as for an inverse acoustics problem. | Cosmin Anitescu Elena Atroshchenko Naif Alajlan Timon Rabczuk | 2019 | Computers, Materials & Continua2019，，4： | 72 |
| 4 | The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies显示文摘The Open Quantum Materials Database(OQMD)is a high-throughput database currently consisting of nearly 300,000 density functional theory(DFT)total energy calculations of compounds from the Inorganic Crystal Structure Database(ICSD)and decorations of commonly occurring crystal structures.To maximise the impact of these data,the entire database is being made available,without restrictions,at www.oqmd.org/download.In this paper,we outline the structure and contents of the database,and then use it to evaluate the accuracy of the calculations therein by comparing DFT predictions with experimental measurements for the stability of all elemental ground-state structures and 1,670 experimental formation energies of compounds.This represents the largest comparison between DFT and experimental formation energies to date.The apparent mean absolute error between experimental measurements and our calculations is 0.096 eV/atom.In order to estimate how much error to attribute to the DFT calculations,we also examine deviation between different experimental measurements themselves where multiple sources are available,and find a surprisingly large mean absolute error of 0.082 eV/atom.Hence,we suggest that a significant fraction of the error between DFT and experimental formation energies may be attributed to experimental uncertainties.Finally,we evaluate the stability of compounds in the OQMD(including compounds obtained from the ICSD as well as hypothetical structures),which allows us to predict the existence of~3,200 new compounds that have not been experimentally characterised and uncover trends in material discovery,based on historical data available within the ICSD. | Scott Kirklin James E Saal Bryce Meredig Alex Thompson Jeff W Doak Muratahan Aykol Stephan Rühl Chris Wolverton | 2015 | npj Computational Materials2015，，1： | 62 |
| 5 | A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate显示文摘In this paper,a deep collocation method(DCM)for thin plate bending problems is proposed.This method takes advantage of computational graphs and backpropagation algorithms involved in deep learning.Besides,the proposed DCM is based on a feedforward deep neural network(DNN)and differs from most previous applications of deep learning for mechanical problems.First,batches of randomly distributed collocation points are initially generated inside the domain and along the boundaries.A loss function is built with the aim that the governing partial differential equations(PDEs)of Kirchhoff plate bending problems,and the boundary/initial conditions are minimised at those collocation points.A combination of optimizers is adopted in the backpropagation process to minimize the loss function so as to obtain the optimal hyperparameters.In Kirchhoff plate bending problems,the C^1 continuity requirement poses significant difficulties in traditional mesh-based methods.This can be solved by the proposed DCM,which uses a deep neural network to approximate the continuous transversal deflection,and is proved to be suitable to the bending analysis of Kirchhoff plate of various geometries. | Hongwei Guo Xiaoying Zhuang Timon Rabczuk | 2019 | Computers, Materials & Continua2019，，5： | 53 |
| 6 | PCT:Point cloud transformer显示文摘The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing.This paper presents a novel framework named Point Cloud Transformer(PCT)for point cloud learning.PCT is based on Transformer,which achieves huge success in natural language processing and displays great potential in image processing.It is inherently permutation invariant for processing a sequence of points,making it well-suited for point cloud learning.To better capture local context within the point cloud,we enhance input embedding with the support of farthest point sampling and nearest neighbor search.Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification,part segmentation,semantic segmentation,and normal estimation tasks. | Meng-Hao Guo Jun-Xiong Cai Zheng-Ning Liu Tai-Jiang Mu Ralph R.Martin Shi-Min Hu | 2021 | Computational Visual Media2021，7，2： | 55 |
| 7 | CONVERGENCE ANALYSIS FOR A NONCONFORMING MEMBRANE ELEMENT ON ANISOTROPIC MESHES显示文摘有限元素网孔的常规假设为常规一致元素 andnonconform-ing 元素两个都是有限元素近似的大多数分析的一个基本条件。这篇论文的目的是介绍处理一个四度的非一致的有限元素的近似的一条新奇途径因为第二在各向异性的网孔上订椭圆形的问题。没有在那里喉部的假设或伪制服假设的精力标准和 L2 标准的最佳的错误估计基于此处发现的这个元素的一些新特殊特征被获得。数字结果被给表明我们的理论分析的有效性。 | Dong-yang Shi Shao-chun Chen Ichiro Hagiwara | 2005 | Journal of Computational Mathematics2005，23，4： | 43 |
| 8 | AN ANISOTROPIC NONCONFORMING FINITE ELEMENT METHOD FOR APPROXIMATING A CLASS OF NONLINEAR SOBOLEV EQUATIONS显示文摘各向异性的 nonconforming 有限元素方法为非线性的 Sobolev 方程的一个班被介绍。最佳的错误估计和 supercloseness 为半分离、充分分离的近似计划被获得,它与传统的有限元素方法一样。另外,全球 superconvergence 通过 postprocessing 技术被导出。数字实验被包括说明建议方法的可行性。[从作者抽象] | Dongyang Shi Haihong Wang Yuepeng Du | 2009 | Journal of Computational Mathematics2009，27，2： | 49 |
| 9 | Recent advances and applications of machine learning in solidstate materials science显示文摘One of the most exciting tools that have entered the material science toolbox in recent years is machine learning.This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research.At present,we are witnessing an explosion of works that develop and apply machine learning to solid-state systems.We provide a comprehensive overview and analysis of the most recent research in this topic.As a starting point,we introduce machine learning principles,algorithms,descriptors,and databases in materials science.We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure.Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning.We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications.Two major questions are always the interpretability of and the physical understanding gained from machine learning models.We consider therefore the different facets of interpretability and their importance in materials science.Finally,we propose solutions and future research paths for various challenges in computational materials science. | Jonathan Schmidt Mário R.G.Marques Silvana Botti Miguel A.L.Marques | 2019 | npj Computational Materials2019，，1： | 49 |
| 10 | A Survey on Deep Learning-based Fine-grained Object Classification and Semantic Segmentation显示文摘The deep learning technology has shown impressive performance in various vision tasks such as image classification, object detection and semantic segmentation. In particular, recent advances of deep learning techniques bring encouraging performance to fine-grained image classification which aims to distinguish subordinate-level categories, such as bird species or dog breeds. This task is extremely challenging due to high intra-class and low inter-class variance. In this paper, we review four types of deep learning based fine-grained image classification approaches, including the general convolutional neural networks(CNNs), part detection based,ensemble of networks based and visual attention based fine-grained image classification approaches. Besides, the deep learning based semantic segmentation approaches are also covered in this paper. The region proposal based and fully convolutional networks based approaches for semantic segmentation are introduced respectively. | Bo Zhao Jiashi Feng Xiao Wu Shuicheng Yan | 2017 | International Journal of Automation and computing2017，14，2： | 36 |
| 11 | Salient object detection: A survey显示文摘Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many models have been proposed and several applications have emerged, a deep understanding of achievements and issues remains lacking. We aim to provide a comprehensive review of recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics for salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance, and suggest future research directions. | Ali Borji Ming-Ming Cheng Qibin Hou Huaizu Jiang Jia Li | 2019 | Computational Visual Media2019，5，2： | 40 |
| 12 | Machine learning in materials informatics:recent applications and prospects显示文摘Propelled partly by the Materials Genome Initiative,and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains,informatics strategies are beginning to take shape within materials science.These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved.Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost,time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases.Predictions are typically interpolative,involving fingerprinting a material numerically first,and then following a mapping(established via a learning algorithm)between the fingerprint and the property of interest.Fingerprints,also referred to as“descriptors”,may be of many types and scales,as dictated by the application domain and needs.Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account.This article attempts to provide an overview of some of the recent successful data-driven“materials informatics”strategies undertaken in the last decade,with particular emphasis on the fingerprint or descriptor choices.The review also identifies some challenges the community is facing and those that should be overcome in the near future. | Rampi Ramprasad Rohit Batra Ghanshyam Pilania Arun Mannodi-Kanakkithodi Chiho Kim | 2017 | npj Computational Materials2017，，1： | 40 |
| 13 | A NONCONFORMING ANISOTROPIC FINITE ELEMENT APPROXIMATION WITH MOVING GRIDS FOR STOKES PROBLEM显示文摘这篇论文被奉献给非与动人的格子遵守有限元素计划的五个参数因为速度压力在 2-D 混合了非静止的 Stokesproblem 的明确的表达。我们证明这个元素有各向异性的行为并且基于一些新奇技术在速度和压力的一些某些标准导出各向异性的错误评价。特别通过我们得到的小心的分析,一致性错误评价上的有趣的结果,它从来没被看见过为混合了有限元素方法在以前文学。 | Dong-yang Shi Yi-ran Zhang | 2006 | Journal of Computational Mathematics2006，24，5： | 33 |
| 14 | ON SPECTRAL METHODS FOR VOLTERRA INTEGRAL EQUATIONS AND THE CONVERGENCE ANALYSIS显示文摘这个工作的主要目的是为 Volterra 不可分的方程提供一条新奇数字途径基于一光谱途径。一个 Legendre 搭配方法被建议解决第二种类型的 Volterra 不可分的方程。如果内核函数和来源函数是足够地光滑的,我们为建议方法提供严密错误分析,它显示数字错误指数地腐烂。数字结果证实领带的理论预言集中的指数的率。在这个工作的结果似乎是第一成功光谱途径(与理论理由) 为 Volterra 类型方程。 | Tao Tang Xiang XU Jin Cheng | 2008 | Journal of Computational Mathematics2008，26，6： | 32 |
| 15 | Optimal Control of Nonlinear Inverted Pendulum System Using PID Controller and LQR: Performance Analysis Without and With Disturbance Input显示文摘Linear quadratic regulator(LQR) and proportional-integral-derivative(PID) control methods, which are generally used for control of linear dynamical systems, are used in this paper to control the nonlinear dynamical system. LQR is one of the optimal control techniques, which takes into account the states of the dynamical system and control input to make the optimal control decisions.The nonlinear system states are fed to LQR which is designed using a linear state-space model. This is simple as well as robust. The inverted pendulum, a highly nonlinear unstable system, is used as a benchmark for implementing the control methods. Here the control objective is to control the system such that the cart reaches a desired position and the inverted pendulum stabilizes in the upright position. In this paper, the modeling and simulation for optimal control design of nonlinear inverted pendulum-cart dynamic system using PID controller and LQR have been presented for both cases of without and with disturbance input. The Matlab-Simulink models have been developed for simulation and performance analysis of the control schemes. The simulation results justify the comparative advantage of LQR control method. | Lal Bahadur Prasad Barjeev Tyagi Hari Om Gupta | 2014 | International Journal of Automation and computing2014，11，6： | 32 |
| 16 | Minimal Gated Unit for Recurrent Neural Networks显示文摘Recurrent neural networks(RNN) have been very successful in handling sequence data.However,understanding RNN and finding the best practices for RNN learning is a difficult task,partly because there are many competing and complex hidden units,such as the long short-term memory(LSTM) and the gated recurrent unit(GRU).We propose a gated unit for RNN,named as minimal gated unit(MGU),since it only contains one gate,which is a minimal design among all gated hidden units.The design of MGU benefits from evaluation results on LSTM and GRU in the literature.Experiments on various sequence data show that MGU has comparable accuracy with GRU,but has a simpler structure,fewer parameters,and faster training.Hence,MGU is suitable in RNN s applications.Its simple architecture also means that it is easier to evaluate and tune,and in principle it is easier to study MGU s properties theoretically and empirically. | Guo-Bing Zhou Jianxin Wu Chen-Lin Zhang Zhi-Hua Zhou | 2016 | International Journal of Automation and computing2016，13，3： | 30 |
| 17 | Current Researches and Future Development Trend of Intelligent Robot: A Review显示文摘With the advancing of industrialization and the advent of the information age, intelligent robots play an increasingly important role in intelligent manufacturing, intelligent transportation system, the Internet of things, medical health and intelligent services. Based on working experiences in and reviews on intelligent robot studies both in China and abroad, the authors summarized researches on key and leading technologies related to human-robot collaboration, driverless technology, emotion recognition, brain-computer interface, bionic software robot and cloud platform, big data network, etc. The development trend of intelligent robot was discussed, and reflections on and suggestions to intelligent robot development in China were proposed. The review is not only meant to overview leading technologies of intelligent robot all over the world, but also provide related theories, methods and technical guidance to the technological and industrial development of intelligent robot in China. | Tian-Miao Wang Yong Tao Hui Liu | 2018 | International Journal of Automation and computing2018，15，5： | 29 |
| 18 | PVT v2:Improved baselines with Pyramid Vision Transformer显示文摘Transformers have recently lead to encouraging progress in computer vision.In this work,we present new baselines by improving the original Pyramid Vision Transformer(PVT v1)by adding three designs:(i)a linear complexity attention layer,(ii)an overlapping patch embedding,and(iii)a convolutional feed-forward network.With these modifications,PVT v2 reduces the computational complexity of PVT v1 to linearity and provides significant improvements on fundamental vision tasks such as classification,detection,and segmentation.In particular,PVT v2 achieves comparable or better performance than recent work such as the Swin transformer.We hope this work will facilitate state-ofthe-art transformer research in computer vision.Code is available at http://gffzz188fe103f8f1460asbpb5uuon905066p5.ffgz.tsg.suse.edu.cn/whai362/PVT. | Wenhai Wang Enze Xie Xiang Li Deng-Ping Fan Kaitao Song Ding Liang Tong Lu Ping Luo Ling Shao | 2022 | Computational Visual Media2022，8，3： | 30 |
| 19 | A SELF-ADAPTIVE TRUST REGION ALGORITHM显示文摘In this paper we propose a self-adaptive trust region algorithm.The trust region radius is updated at a varable rate according to the ratio between the actual reduction and the predicted reduction of the objective function,rather than by simply enlarging or reducing the original trust region radius at a constant rate.We show that this new algorithm preserves the strong convergence property of traditional trust region methods.Numerical results are also presented. | Long Hei (Institute of Computational Mathematics and Scientific/Engineering Computing, Academy ofMathematics and Systems Sciences, Chinese Academy of Sciences, Beijing 100080, China)(Department of Industrial Engineering and Management Sciences Northwestern University C2SO,2145 Sheridan Road Evanston, Illinois 60208, USA) | 2003 | Journal of Computational Mathematics2003，21，2： | 29 |
| 20 | ASYMPTOTIC ERROR EXPANSION AND DEFECT CORRECTION FOR SOBOLEV AND VISCOELASTICITY TYPE EQUATIONS显示文摘In this paper we study the higher accuracy methods-the extrapolation and defect correction for the semidiscrete Galerkin approximations to the solutions of Sobolev and viscoelasticity type equations. The global extrapolation and the correction approximations of third order, rather than the pointwise extrapolation results are presented. | Qun Lin Shu-hua Zhang Ning-ning Yan(Institute of Systems Science, Chinese Academy of Sciences, Beijing, China) | 1998 | Journal of Computational Mathematics1998，16，1： | 28 |
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