Developing constitutive models from EPR‐based self‐learning finite element analysis          下载免费PDF全文

Ali Nassr  Akbar Javadi  Asaad Faramarzi 《国际地质力学数值与分析法杂志》2018,42(3):401-417

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.  相似文献   

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing     

Peng Yue  Fan Gao  Boyi Shangguan  Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.  相似文献   

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning     

Olac Fuentes 《Experimental Astronomy》2001,12(1):21-31

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.  相似文献   

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms   总被引：1，自引：0，他引：1  

J. Federico Ramírez  Olac Fuentes  Ravi K. Gulati 《Experimental Astronomy》2001,12(3):163-178

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.  相似文献   

太湖平原西北部全新世古河道沉积特征及环境演变   总被引：1，自引：0，他引：1  

舒军武  王伟铭  陈晔 《地层学杂志》2008,32(2):146-152

粒度、磁化率定量数据分析在判明第四系沉积物的来源、识别沉积分异作用、解译古沉积相和环境方面有着重要作用。对太湖平原西北部卜弋桥ZK01钻孔上部全新统如东组进行了粒度和磁化率测试分析,结合该孔孢粉成果,揭示研究区全新世古河道沉积环境和气候明显经历了3个阶段:1)11000—9500cal.aBP,沉积物主要由粉砂组成,粒度证据表明水动力条件较强,孢粉证据显示该阶段气候较温暖湿润;2)9500—3900cal.aBP,沉积物砂组分含量大幅增加,粒度资料显示古河道水动力明显增强,孢粉植被表明该时期处于全新世大暖期,气候暖湿稳定;3)3900cal.aBP以来,粉砂组分在沉积物中占优势,粒度结果表明该阶段水动力显著减弱,孢粉资料和磁化率证据表明研究区自该阶段特别是后期以来逐步出现了明显的人类活动迹象。  相似文献   

水下智能潜器的神经网络运动控制   总被引：10，自引：4，他引：10  

彭良  卢迎春  万磊  孙俊岭 《海洋工程》1995,(2)

本文介绍一种基于神经网络的水下智能潜器的运动控制方法，该方法通过在线学习，融控制与滤波为一体。计算机仿真与水池实验验证表明，该方法的控制与滤波性能良好，对环境的学习与适应能力强。该方法事实上可用于一般动力系统的控制。  相似文献   

好氧颗粒污泥系统快速启动试验研究   总被引：2，自引：0，他引：2  

王琳  孙磊  林跃梅 《中国海洋大学学报(自然科学版)》2006,36(2):299-302

采用3组圆柱型SBR反应器进行活性污泥颗粒化培养,接种浓度分别为12 500mg.L-1,4 400mg.L-1和1 800mg.L-1。由于采用逐步缩短沉降时间这一有效措施,系统启动20d后,颗粒污泥初步形成,污泥浓度可达到6 000~8 000mg.L-1,高于普通活性污泥系统,且沉降性能良好,SVI值在50mL.g-1左右。颗粒污泥的平均粒径稳定在0.2~0.25mm。在活性污泥颗粒化的启动试验中,在生物化学条件相同的条件下,逐步缩短沉淀时间可以加快好氧污泥颗粒化系统的启动。  相似文献   

流形上的空间密度聚类算法研究     

刘峰  刘希玉  刘弘 《中国海洋大学学报(自然科学版)》2007,37(4):681-684,626

研究流形上的聚类分析，针对基于密度的空间聚类引入了流形概念，提出1种基于流形的密度聚类算法，该方法将流形的概念与聚类相结合，可以适用于样本为复杂分布的聚类。文中通过实例证明此算法的有效性。  相似文献   

A brief catalogue of failures: Framing evaluation and learning in fisheries resource management     

Kre Nolde Nielsen  Petter Holm 《Marine Policy》2007,31(6):669-680

We propose a framework for enabling a systematic evaluation of a fisheries resource management system, which we define as a feed-back mechanism coupled to a fishery. The resource management system includes four basic functions: diagnostics, intervention, goal setting, and decision making. This model allows for the development of an evaluation framework for fisheries resource management by facilitating a typology of failures. We suggest that the potential for systemic and interdisciplinary learning will be significantly enhanced through the process of developing such a framework.  相似文献   

空间智能:地理信息科学的新进展   总被引：5，自引：1，他引：4  

黄波  吴波  刘彪  曹凯 《遥感学报》2008,12(5)

在总结多年来研究GIS智能计算的理论与实践基础上,提出地理信息科学发展的新方向:空间智能.空间智能强调发现与应用空间模式,以增强GIS处理复杂数据和解决复杂问题的能力.空间智能主要的技术体系由空间分析、空间优化和空间模拟三大模块构成,其技术基础包括空间统计与索引、智能代理、高级启发式,以及数学规划等系列智能技术.由于空间智能融合了机器学习、统计分析和人工智能等多个学科理论,面向解决实际工程需求中大量存在的复杂时空问题,因此理论上具有广阔的发展空间,实践上也有重大的应用需求.随着空间智能体系的完善和技术的进一步成熟,它将在实际应用中具有巨大的价值.  相似文献