The original online version of this article (Randrianarivony, M. (June 2014) On DFT Molecular Simulation for Non-Adaptive Kernel Approximation. Advances in Materials Physics and Chemistry, Vol. 4 No. 6, 105-115. http:...
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The original online version of this article (Randrianarivony, M. (June 2014) On DFT Molecular Simulation for Non-Adaptive Kernel Approximation. Advances in Materials Physics and Chemistry, Vol. 4 No. 6, 105-115. http://***/10.4236/ampc.2014.46013) did not contain any acknowledgment. The author wishes to add the following acknowledgements: Acknowledgements: This work was partially supported by Eurostars Project E!6935 funded by German Federal Ministry of Education and Research.
Robust optimization determines how the input variables dispersion is propagated on the output variables. This is of great practical relevance: For example, the quality of a product is influenced decisively by producti...
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ISBN:
(纸本)9789609999465
Robust optimization determines how the input variables dispersion is propagated on the output variables. This is of great practical relevance: For example, the quality of a product is influenced decisively by production tolerances. In industrial applications it is important to characterize the range of variation with appropriate measures. The industry is particularly interested in accurate limits of the output distribution or its centered part. In this article the mathematical characterization of robustness is discussed under the viewpoint of its practical applicability. It is shown, that the usually used robustness measures mean for central tendency and standard deviation for dispersion produce inaccurate limits. Instead several measures based on quantiles are proposed. The median is used as measure of central tendency while different quantile ranges are used as measure of dispersion. They are compared for the robust optimization of mathematical functions and industrial applications. The advantages of the quantile measures are pointed out. The computation of quantiles is expensive, because it needs many function evaluations. Due to their long runtime only a few simulations can be executed in practice. A methodology tailored to this situation is proposed. It is based on the use of metamodels. Starting with a few real simulations a metamodel for the system is build. Further ones for median and dispersion of the output variables are derived from it. This enables the user to perform a full multicriteria robust optimization on the whole parameter range. The methodology takes the tolerances of the metamodels into account. A new measure for the tolerance of metamodels which are derived from metamodels is presented. It is used to estimate the accuracy of the quantile models. The tolerance can easily be integrated in the robust optimization process.
Quantum energies which are used in applications are usually composed of repulsive and attractive terms. The objective of this study is to use an accurate and efficient fitting of the repulsive energy instead of using ...
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Quantum energies which are used in applications are usually composed of repulsive and attractive terms. The objective of this study is to use an accurate and efficient fitting of the repulsive energy instead of using standard parametrizations. The investigation is based on Density Functional Theory and Tight Binding simulations. Our objective is not only to capture the values of the repulsive terms but also to efficiently reproduce the elastic properties and the forces. The elasticity values determine the rigidity of a material when some traction or load is applied on it. The pair-potential is based on an exponential term corrected by B-spline terms. In order to accelerate the computations, one uses a hierarchical optimization for the B-splines on different levels. Carbon graphenes constitute the configurations used in the simulations. We report on some results to show the efficiency of the B-splines on different levels.
Using accurate quantum energy computations in nanotechnologic applications is usually very computationally intensive. That makes it difficult to apply in subsequent quantum simulation. In this paper, we present some p...
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Using accurate quantum energy computations in nanotechnologic applications is usually very computationally intensive. That makes it difficult to apply in subsequent quantum simulation. In this paper, we present some preliminary results pertaining to stochastic methods for alleviating the numerical expense of quantum estimations. The initial information about the quantum energy originates from the Density Functional Theory. The determination of the parameters is performed by using methods stemming from machine learning. We survey the covariance method using marginal likelihood for the statistical simulation. More emphasis is put at the position of equilibrium where the total atomic energy attains its minimum. The originally intensive data can be reproduced efficiently without losing accuracy. A significant acceleration gain is perceived by using the proposed method.
The VERCE project has pioneered an e-Infrastructure to support researchers using established simulation codes on high-performance computers in conjunction with multiple sources of observational data. This is accessed ...
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The VERCE project has pioneered an e-Infrastructure to support researchers using established simulation codes on high-performance computers in conjunction with multiple sources of observational data. This is accessed and organised via the VERCE science gateway that makes it convenient for seismologists to use these resources from any location via the Internet. Their data handling is made flexible and scalable by two Python libraries, ObsPy and dispel4py and by data services delivered by ORFEUS and EUDAT. Provenance driven tools enable rapid exploration of results and of the relationships between data, which accelerates understanding and method improvement. These powerful facilities are integrated and draw on many other e-Infrastructures. This paper presents the motivation for building such systems, it reviews how solid-Earth scientists can make significant research progress using them and explains the architecture and mechanisms that make their construction and operation achievable. We conclude with a summary of the achievements to date and identify the crucial steps needed to extend the capabilities for seismologists, for solid-Earth scientists and for similar disciplines.
Crash simulation results show both deterministic and stochastic behavior. For optimization in automotive design it is very important to distinguish between effects caused by variation of simulation parameters and effe...
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In order to build networks for systems biology from the literature an UIMA based extraction workflow using various named entity recognition processes and different relation extraction methods has been composed. The Un...
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We present new results in stochastic multi-objective optimization applied to focused ultrasonic therapy planning. This type of non-invasive therapy uses focused ultrasound for the destruction of tumor cells and magnet...
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ISBN:
(纸本)9789898565693
We present new results in stochastic multi-objective optimization applied to focused ultrasonic therapy planning. This type of non-invasive therapy uses focused ultrasound for the destruction of tumor cells and magnetic resonance tomography for identification of tumor volume and healthy organs. During the therapy planning the treatment parameters, such as frequency and intensity of ultrasound, are adjusted to achieve maximal tumor destruction and minimal influence to the healthy organs. For this purpose multi-objective optimization is used. RBF metamodeling is employed for continuous representation of discretely sampled results of numerical simulation and for evaluation of inherent uncertainties. We apply two algorithms for multi-objective optimization capable of non-convex Pareto front detection in the considered problem. Cross-validation procedure and sensitivity analysis are used for estimation of uncertainties. A realistic application case demonstrates the efficiency of the approach.
The large impact of civil water engineering to nature and society imposes high requirements for the precision of numerical simulations used in planning and evaluation of river engineering concepts. In particular, coup...
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ISBN:
(纸本)9789898565693
The large impact of civil water engineering to nature and society imposes high requirements for the precision of numerical simulations used in planning and evaluation of river engineering concepts. In particular, coupled morphodynamic - hydrodynamic simulation uses models of river bed evolution possessing uncertain parameters. The sources of uncertainty can be the natural variability, the deficient description of the physical processes in the model and the imprecision of the model parameters. The propagation of these uncertainties to the variance of the model result can be quantified with the aid of stochastic analysis. Precise evaluation of stochastic characteristics normally requires a huge amount of samples, which can be provided by surrogate-based modeling of simulation results. In this paper we present our advances in quantile estimation of morphodynamic simulations of river bed evolution. We use metamodeling of bulky simulation results with radial basis functions (RBF), quasi-Monte Carlo sampling (QMC) and efficient quantile estimator (QE). Four different quantile estimators have been tested. A realistic application case is used to demonstrate the efficiency of the approach.
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