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   

Method for evaluating the degrees of land use sustainability of mountainous county and its application in Yunnan Province,China   总被引：4，自引：1，他引：3  

Zisheng Yang  Yansui Liu  Wenxing Tao  Jingjing Xu  Qiaogui Zhao 《山地科学学报》2008,5(2):98-112

The evaluation of sustainable land use is the key issue in the field of studying the sustainable land utilization. In general analysis, the sustainable land use is evaluated respectively from its ecological sustainability, economic sustainability and social sustainability in China and other countries in recent years. Although this evaluation is an important work, it seems insufficient and hard to comprehensively reflect the whole degree of land use sustainability. Thus, to make up this deficiency, this paper brings forward the evaluation indexes, which make it possible to quantitatively reflect the whole degree of land use sustainability, namely, the concept of ＂degrees of overall land use sustainability＂ （Dos）, and research and measurement development of the method of and calculation in Dos. Taking the evaluation of the degree of land use sustainability in county regions of Yunnan Province as the actual example for analysis, results are basically as follows：
1） The degree of land use sustainability （Dos） is the ration index to organically and systematically integrate the degree of ecological friendliness （DeF）, the degree of economic viability （Dev） and the degree of social acceptability （Dsa）, able to comprehensively reflect the whole sustainability degree of regional land use
2） Based on the value of Dos, the grading system and standard for the sustainability of land use may be established and totally divided into five grades, namely, the high-degree sustainability, middle-degree sustainability, low-degree sustainability, conditional sustainability and non-sustainability. Meanwhile, the standard for distinguishing sustainability grades has also been confirmed so as to determine the nature of sustainability degrees in different grades. This makes the possibility for the combination of nature determination with ration in research result and provides with the scientific guideline and decision-making gist for better implementation of sustainable land use strategy.
3） The pract  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   

Models in geography revisited     

N.J. Clifford 《Geoforum》2008,39(2):675-686

Agent based models (ABMs) have many applications, and illustrate a rapidly developing field of enquiry, spanning both the physical-mathematical and human-social sciences. ABMs are seen as most appropriate in situations where decisions or actions are distributed around particular locations, and in which structure is viewed as emergent from the interaction between individuals. ABMs may be used either as representational devices, to reproduce the patterns observed or desired in the system of interest, or as foundational tools contributing to the development of social or economic theory. The role and status of models and modelling is itself an instantiation of a wider debate concerning representation and explanation. Today, a case can be made that the nature of explanation and the use of scientific interpretation reflect much less definite and exclusive positions and permit more diverse approaches than hitherto. The underlying proposition of this commentary is, therefore, that the time is right for a positive application of ABMs within the discipline of geography, and for a rediscovery and reappraisal of the richness and depth of insight in the model-building enterprise more generally. First, the context for ABM development and application is set with reference to the agency-structure debate. Second, some aspects of the heritage of models in geography is presented, based upon reviews of two benchmark publications bearing that title. Next, some of the most significant characteristics, uses, potentials and limitations of ABMs, are reviewed. Finally, some constructive ways forward are suggested, as informed by theory and method from the interpretative social sciences.  相似文献   

GMOs and their contexts: A comparison of potential and actual performance of GM crops in a local agricultural setting     

A. Wendy Russell 《Geoforum》2008,39(1):213-222

In this paper, I argue that genetically modified organisms (GMOs) have inherent potential to contribute to socially and environmentally sustainable agriculture by virtue of their ‘biological embeddedness’. Their actual ‘performance’ and how this contributes to sustainability depends on the ‘mutual shaping’ of technology and context. While much attention has been given to the design context of GMOs, this paper considers the influence of the application context and of users. A case study investigating the use of insect-resistant and herbicide-tolerant GM cotton in the cotton-growing region of New South Wales in Australia is presented. The study was based on focus groups with farmers and other stakeholders in a cotton-growing community. It demonstrated a range of direct and indirect effects of GM cotton use, both positive and negative for sustainability, and the ways in which these effects were influenced by the local social context. The influences of the biotechnology industry context, in limiting the contributions that gene technologies can make to sustainability, were also considered, and remedies suggested. I argue that the polarity of the GM debate is hindering progress on these issues, and that a more balanced approach to our analysis of GMOs is necessary in order to fully understand, and to influence, their role in the future of rural spaces.  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   

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