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1.
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
2.
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
3.
This paper briefly reviews the formulations used over the last 40 years for the solution of problems involving tensile cracking, with both the discrete and the smeared crack approaches. The paper focuses on the smeared approach, identifying as its main drawbacks the observed mesh‐size and mesh‐bias spurious dependence when the method is applied ‘straightly’. A simple isotropic local damage constitutive model is considered, and the (exponential) softening modulus is regularized according to the material fracture energy and the element size. The continuum and discrete mechanical problems corresponding to both the weak discontinuity (smeared cracks) and the strong discontinuity (discrete cracks) approaches are analysed and the question of propagation of the strain localization band (crack) is identified as the main difficulty to be overcome in the numerical procedure. A tracking technique is used to ensure stability of the solution, attaining the necessary convergence properties of the corresponding discrete finite element formulation. Numerical examples show that the formulation derived is stable and remarkably robust. As a consequence, the results obtained do not suffer from spurious mesh‐size or mesh‐bias dependence, comparing very favourably with those obtained with other fracture and continuum mechanics approaches. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
4.
Anita Enmark Thomas Berkefeld Oskar von der Lühe Torben Andersen 《Experimental Astronomy》2006,21(2):87-99
A simulation model of the adaptive optics of the German Vacuum Tower Telescope (VTT), Observatorio del Teide, Tenerife, is
presented. The model uses modules from the integrated model of the Euro50 extremely large telescope, and includes submodels
of a Shack-Hartmann wavefront sensor, a de-formable mirror, a tip-tilt mirror, high-voltage amplifier low-pass filters, a
reconstructor and a controller. We investigate the impact on the closed loop bandwidth of changes in controller configuration
and certain system parameters, such as low pass filter bandwidth and camera integration and readout time. Control strategies
were tested on simple models before implementation on the full VTT model. Using the models, different control strategies are
compared. 相似文献
5.
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
6.
Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms 总被引:1,自引:0,他引:1
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
7.
用遗传算法解算病态方程 总被引:7,自引:1,他引:6
对应用遗传算法解决病态方程问题进行了探讨。利用拟合法而不是通过法方程求解参数,从而避免了法方程系数求逆,使病态方程的解答有了较好的结果。通过模拟计算并和其他方法进行比较,证明该方法是可行的和有效的。 相似文献
8.
T. G. Sitharam Pijush Samui P. Anbazhagan 《Geotechnical and Geological Engineering》2008,26(5):503-517
Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock
is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site
amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore,
India is arrived from the 652 boreholes data in the area covering 220 km2. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability
of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector
Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of
rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore,
where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the
Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation
algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine
based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the
reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced
level of rock has been presented and discussed. 相似文献
9.
10.
洪忠渝 《中国海洋大学学报(自然科学版)》1994,(Z2)
设计两种计算数据域特征值的算法。此特征值通常是用线性反馈移位寄存器(LF-SR)组成的特征分析器得到的.在数据已按字节存放和速度要求不高的场合下,本算法将是十分方便和有用的. 相似文献