Developing constitutive models from EPR‐based self‐learning finite element analysis

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.     

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

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.     

BRINGING FORWARD OF THE CONCEPT OF COGIS AND ITS ARCHITECUTRE RESEARCH

Based on the analysis of the development of GIS technology and application,this paper brought forward the concept of GoGIS,namely Cooperative GIS ,CoGIS is GIS facing group-users and supporting human-human interaction,which makes it differ from the former GISs,Then,the characteristics of general Computer Spport Cooperative Work (CSCW)applications and the complexity of Geographic Information Science were analyzed,and the conclusion the CoGIS was not a simple GIS layer on CSCW was reached,Further,this paper gaver the hierarchical architecture of CoGIS,and analyzed the coperative platform in detail from the following:1)basic elements;2) collaboration patterns;3) cooperation control mechanism;4) synchronization mechanism;5) security and 6) group communication and so on.With those,the problems about GIS applications are discussed,such as 1)distributed multi-source GIS information and knowledge sharing platform;2)the fusion and visualization of GIS information;3)virtual reality cooperative modeling;4) dymamic simulation;5)expert system and 6) decision-making.Finally,this paper analyzed CoGIS application mode in brief.     

Tidal dwarfs in the M81 group: the second generation?

We present a quantitative star formation history derivation of the four suspected tidal dwarf galaxies in the M 81 group: Holmberg IX, BK3N,Arp-loop (A0952+69) and Garland using HST/WFPC2 images of these galaxies. We construct a library of synthetic Colour-Magnitude Diagrams(CMDs) based on theoretical isochrones and data-derived determinations of photometric errors. These synthetic CMDs were combined linearly andχ²-compared to observed photometry. All the galaxies show continuous star formation between about 20 and 200 Myr ago with star formation rates between 7.5⋅10^-3 M_⊙/yr and 7.67⋅10^-4 M_⊙/yr. The metallicity of the detected stars is spanning rather a wide range, being lower than solar abundance. We suppose, that all the galaxies were formed out of material from metal-poor outer part of the giant spiral galaxy M81after tidal interaction about 200 Myr ago. However, this suggestion requires significantly more deep color-magnitude diagrams to be sure with the scenario of the galaxy evolution. This revised version was published online in July 2006 with corrections to the Cover Date.     

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

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.     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

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.     

宁波商帮的海洋文化情结

宁波商帮是我国十大商帮之一 ,百年经久不衰 ,究其原因是宁波商帮受到独特的宁波城市文化熏陶。宁波得天独厚的海洋优势 ,造就了有悠久历史凝结和中外文化交融的宁波海洋文化。海洋文化是一种先进的文化 ,具有大海的许多秉性 :大度、创新、包容。宁波城市文化把中国传统的儒家文化和宁波海洋文化很好地结合起来。正是这种独特的宁波城市文化 ,才哺育出顽强开拓、勇于创新 ,克勤克俭、脚踏实地 ,注重团队、和衷共济的“宁波商帮精神”。     

Crustal and upper mantle structures of the brazilian coast

The Rayleigh wave phase and group velocities in the period range of 24–39 sec, obtained from two earthquakes which occurred in northeastern brazil and which were recorded by the Brazilian seismological station RDJ (Rio de Janeiro), have been used to study crustal and upper mantle structures of the Brazilian coastal region. Three crustal and upper mantle models have been tried out to explain crustal and upper mantle structures of the region. The upper crust has not been resolved, due basically to the narrow period range of the phase and group velocities data. The phase velocity inversions have exhibited good resolutions for both lower crust and upper mantle, with shear wave velocities characteristic of these regions. The group velocity data inversions for these models have showed good results only for the lower crust. The shear wave velocities of the lower crust (3.86 and 3.89 km/sec), obtained with phase velocity inversions, are similar to that (=3.89 km/sec) found byHwang (1985) to the eastern South American region, while group velocity inversions have presented shear velocity (=3.75 km/sec) similar to that (=3.78 km/sec) found byLazcano (1972) to the Brazilian shield. It was not possible to define sharply the crust-mantle transition, but an analysis of the phase and group velocity inversions results has indicated that the total thickness of the crust should be between 30 and 39 km. The crustal and upper mantle model, obtained with phase velocity inversion, can be used as a preliminary model for the Brazilian coast.     

The Mordor Alkaline Igneous Complex,Central Australia: PGE-enriched disseminated sulfide layers in cumulates from a lamprophyric magma

The Mordor Alkaline Igneous Complex (MAIC) is a composite intrusion comprising a body of syenite and a funnel-shaped layered mafic–ultramafic intrusion of lamprophyric parentage, the Mordor Mafic–Ultramafic Intrusion or MMUI. The MMUI is highly unusual among intrusions of lamprophyric or potassic parentage in containing primary magmatic platinum-group element (PGE)-enriched sulfides. The MMUI sequence consists largely of phlogopite-rich pyroxenitic cumulates, with an inward dipping conformable layer of olivine-bearing cumulates divisible into a number of cyclic units. Stratiform-disseminated sulfide accumulations are of two types: disseminated layers at the base of cyclic units, with relatively high PGE tenors; and patchy PGE-poor disseminations within magnetite-bearing upper parts of cyclic units. Sulfide-enriched layers at cycle bases contain anomalous platinum group element contents with grades up to 1.5 g/t Pt+Pd+Au over 1-m intervals, returning to background values of low parts per billion (ppb) on a meter scale. They correspond to reversals in normal fractionation trends and are interpreted as the result of new magma influxes into a continuously replenished magma chamber. Basal layers have decoupled Cu and PGE peaks reflecting increasing PGE tenors up-section, due to increasing R factors during the replenishment episode, or progressive mixing of between resident PGE-poor magma and more PGE-enriched replenishing magma. The presence of PGE enriched sulfides in cumulates from a lamprophyric magma implies that low-degree partial melts do not necessarily leave sulfides and PGEs in the mantle restite during partial melting. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

苏北盘石山、练山地幔捕虏体的PGE地球化学

通过锍镍火试金预富集法,分析了位于郯庐断裂带东侧的盘石山、练山地幔橄榄岩包体中铂族元素(PGE)和Au含量.不同于部分熔融残留成因地幔橄榄岩中通常所观察到的负斜率型或平坦型的分布模式,这两地的地幔橄榄岩以Pt、Pd、Ru相对富集,Ir、Rh相对亏损的"燕子型"分布模式为特征.Pt、Pd等不相容元素富集说明上地幔除经历过早期的部分熔融外,还经历了后期富Pt、Pd的高熔/岩比的熔(流)体的层析分离交代作用影响.盘石山地幔橄榄岩的PGE总量比练山高,Os的含量也比原始地幔值高;而练山地幔橄榄岩的Os含量比原始地幔值低,说明交代作用带走了练山地幔橄榄岩中的Os,却没有很大改变盘石山地幔橄榄岩中的Os含量,这可能与交代熔(流)体含硫量饱和程度有关.Rh的负异常可能与部分熔融过程中熔体较低的fo2有关.