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Добірка наукової літератури з теми "Methods of three-dimensional optimization"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "Methods of three-dimensional optimization"

Brune, Alexander, and Michal Kočvara. "On Barrier and Modified Barrier Multigrid Methods for Three-Dimensional Topology Optimization." SIAM Journal on Scientific Computing 42, no. 1 (2020): A28—A53. http://dx.doi.org/10.1137/19m1254490.

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Rebai, Maher, Matthieu le Berre, Faicel Hnaien, and Hichem Snoussi. "Exact Biobjective Optimization Methods for Camera Coverage Problem in Three-Dimensional Areas." IEEE Sensors Journal 16, no. 9 (2016): 3323–31. http://dx.doi.org/10.1109/jsen.2016.2519451.

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Shang, Kun, and Kai Zhang. "Three-Dimensional Sonar Imaging Software on the DSP." Advanced Materials Research 219-220 (March 2011): 49–52. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.49.

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This paper has explored the design of three-dimensional imaging sonar software and its application to the hardware platform. In the process of designing, the software has been optimized in view of characteristics of the hardware structures and calculation of the TMS320C6416, and the specific optimization methods and effects have also been presented. It turns out that the effect of software optimization is obvious and the working system stable and reliable.

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Simonetti, Hélio Luiz, Valério Silva Almeida, Francisco de Assis das Neves, Vírgil Del Duca Almeida, and Luttgardes de Oliveira Neto. "Reliability-Based Topology Optimization: An Extension of the SESO and SERA Methods for Three-Dimensional Structures." Applied Sciences 12, no. 9 (2022): 4220. http://dx.doi.org/10.3390/app12094220.

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This study takes an approach to reliability-based topology optimization (RBTO) for 3D structures by applying an expansion of smoothing evolutionary structural optimization (SESO) and sequential element rejection and admission (SERA) for three-dimensional optimization. In the search for the stable optimal solution, and a more reliable structure, we present a performance index with the ability to monitor the evolutionary optimization procedure and adopt the filtering scheme usually applied in solid isotropic material with penalization (SIMP). The limit state functions are the maximum displacement constraints imposed in the topology optimization procedure and a structure’s performance control; a comparative analysis of the deterministic topology optimization (DTO) with the RBTO models is also explored. The obtained results suggest the importance of using the RBTO concept in 3D structures as part of the design analysis process.

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Zangeneh, M., A. Goto, and H. Harada. "On the role of three-dimensional inverse design methods in turbomachinery shape optimization." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 213, no. 1 (1999): 27–42. http://dx.doi.org/10.1243/0954406991522167.

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The application of a three-dimensional (3D) inverse design method in which the blade geometry is computed for a specified distribution of circulation to the design of turbomachinery blades is explored by using two examples. In the first instance the method is applied to the design of radial and mixed flow impellers to suppress secondary flows. Based on our understanding of the fluid dynamics of the flow in the impeller, simple guidelines are developed for input specification of the inverse method in order to systematically design impellers with suppressed secondary flows and a more uniform exit flow field. In the second example the method is applied to the design of a vaned diffuser. Again based on the understanding of the detailed flow field in the diffuser obtained by using 3D viscous calculations and oil flow visualizations, simple design guidelines are developed for input specification to the inverse method in order to suppress corner separation. In both cases the guidelines are verified numerically and in the case of the diffuser further experimental validation is presented.

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Chen, L. L., H. Lian, Z. Liu, H. B. Chen, E. Atroshchenko, and S. P. A. Bordas. "Structural shape optimization of three dimensional acoustic problems with isogeometric boundary element methods." Computer Methods in Applied Mechanics and Engineering 355 (October 2019): 926–51. http://dx.doi.org/10.1016/j.cma.2019.06.012.

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von den Hoff, Bieke, Roberto Merino-Martínez, Dick G. Simons, and Mirjam Snellen. "Using global optimization methods for three-dimensional localization and quantification of incoherent acoustic sources." JASA Express Letters 2, no. 5 (2022): 054802. http://dx.doi.org/10.1121/10.0010456.

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Complex acoustic systems typically present three-dimensional distributions of noise sources. Conventional acoustic imaging methods with planar microphone arrays are unsuitable for three-dimensional acoustic imaging, given the computational demands and the incapability to explicitly account for the presence of multiple sources. This paper proposes the use of global optimization methods to solve these shortcomings. An experiment with three incoherent speakers proved that this method can accurately determine the three-dimensional location and the respective sound level of each individual source. In addition, super-resolution is achieved beyond half the Rayleigh resolution limit.

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TAKAHASHI, Toko, Takahiro FUJIKAWA, and Koichi YONEMOTO. "Investigations of Shape Definition and Aerodynamics Analysis Methods for Three-Dimensional Aerodynamic Shape Optimization." Proceedings of Conference of Kyushu Branch 2019.72 (2019): C21. http://dx.doi.org/10.1299/jsmekyushu.2019.72.c21.

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Schoen, Fabio. "Global optimization methods for high-dimensional problems." European Journal of Operational Research 119, no. 2 (1999): 345–52. http://dx.doi.org/10.1016/s0377-2217(99)00136-8.

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Xiao, Yanyang, Zhonggui Chen, Zhengtao Lin, et al. "Merge-Swap Optimization Framework for Supervoxel Generation from Three-Dimensional Point Clouds." Remote Sensing 12, no. 3 (2020): 473. http://dx.doi.org/10.3390/rs12030473.

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Surpervoxels are becoming increasingly popular in many point cloud processing applications. However, few methods have been devised specifically for generating compact supervoxels from unstructured three-dimensional (3D) point clouds. In this study, we aimed to generate high quality over-segmentation of point clouds. We propose a merge-swap optimization framework that solves any supervoxel generation problem formulated in energy minimization. In particular, we tailored an energy function that explicitly encourages regular and compact supervoxels with adaptive size control considering local geometric information of point clouds. We also provide two acceleration techniques to reduce the computational overhead. The performance of the proposed merge-swap optimization approach is superior to that of previous work in terms of thorough optimization, computational efficiency, and practical applicability to incorporating control of other properties of supervoxels. The experiments show that our approach produces supervoxels with better segmentation quality than two state-of-the-art methods on three public datasets.

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Дисертації з теми "Methods of three-dimensional optimization"

Hambric, Stephen A. "Structural shape optimization of three dimensional finite element models." Thesis, Virginia Tech, 1987. http://hdl.handle.net/10919/45805.

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The thesis presents a three dimensional shape optimization program which analyzes models made up of linear isoparametric elements. The goal of the program is to achieve a near uniform model stress state and thereby to minimize material volume. The algorithm is iterative, and performs two analyses per iteration. The first analysis is a static stress analysis of the model for one or more load cases. Based on results from the static analysis, an expansion analysis is performed. Model elements are expanded or contracted based on whether they are stressed higher or lower than a reference stress. The shape changing is done by creating an expansion load vector using the differences between the calculated element stresses and the reference stress. Expansion displacements are solved for, and instead of using them to calculate stresses, the displacements are added to the nodal coordinates to reshape the structure. This process continues until a user defined convergence tolerance is met. Four programs were used for the analysis process. Models were created using a finite element modeling program called I-IDEAS or CAIEDS. The I-IDEAS output files were converted to input files for the optimizer by a conversion program. The model was optimized using the shape optimization process described above. Post- processing was done using a program written with a graphical programming language called graPHIGS. Models used to test the program were: a cylindrical pressure vessel with nonuniform thickness, a spherical pressure vessel with non-uniform thickness, a torque arm, and a draft sill casting o a railroad hopper car. Results were compared to similar studies from selected references. Both pressure vessels converged to near uniform thicknesses, which compared ell with the reference work. In a two dimensional analysis, the torque arm volume decreased 24 percent, which compared well with published results. A three dimensional analysis showed a volume reduction of l3 percent, but there were convergence problems. Finally, the draft sill casting was reduced in volume by 9 percent from a manually optimized design. Master of Science

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Singley, Andrew M. "Heuristic solution methods for the 1-dimensional and 2-dimensional mastermind problem." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0010554.

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Ren, Xuchun. "Novel computational methods for stochastic design optimization of high-dimensional complex systems." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/1738.

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The primary objective of this study is to develop new computational methods for robust design optimization (RDO) and reliability-based design optimization (RBDO) of high-dimensional, complex engineering systems. Four major research directions, all anchored in polynomial dimensional decomposition (PDD), have been defined to meet the objective. They involve: (1) development of new sensitivity analysis methods for RDO and RBDO; (2) development of novel optimization methods for solving RDO problems; (3) development of novel optimization methods for solving RBDO problems; and (4) development of a novel scheme and formulation to solve stochastic design optimization problems with both distributional and structural design parameters. The major achievements are as follows. Firstly, three new computational methods were developed for calculating design sensitivities of statistical moments and reliability of high-dimensional complex systems subject to random inputs. The first method represents a novel integration of PDD of a multivariate stochastic response function and score functions, leading to analytical expressions of design sensitivities of the first two moments. The second and third methods, relevant to probability distribution or reliability analysis, exploit two distinct combinations built on PDD: the PDD-SPA method, entailing the saddlepoint approximation (SPA) and score functions; and the PDD-MCS method, utilizing the embedded Monte Carlo simulation (MCS) of the PDD approximation and score functions. For all three methods developed, both the statistical moments or failure probabilities and their design sensitivities are both determined concurrently from a single stochastic analysis or simulation. Secondly, four new methods were developed for RDO of complex engineering systems. The methods involve PDD of a high-dimensional stochastic response for statistical moment analysis, a novel integration of PDD and score functions for calculating the second-moment sensitivities with respect to the design variables, and standard gradient-based optimization algorithms. The methods, depending on how statistical moment and sensitivity analyses are dovetailed with an optimization algorithm, encompass direct, single-step, sequential, and multi-point single-step design processes. Thirdly, two new methods were developed for RBDO of complex engineering systems. The methods involve an adaptive-sparse polynomial dimensional decomposition (AS-PDD) of a high-dimensional stochastic response for reliability analysis, a novel integration of AS-PDD and score functions for calculating the sensitivities of the failure probability with respect to design variables, and standard gradient-based optimization algorithms, resulting in a multi-point, single-step design process. The two methods, depending on how the failure probability and its design sensitivities are evaluated, exploit two distinct combinations built on AS-PDD: the AS-PDD-SPA method, entailing SPA and score functions; and the AS-PDD-MCS method, utilizing the embedded MCS of the AS-PDD approximation and score functions. In addition, a new method, named as the augmented PDD method, was developed for RDO and RBDO subject to mixed design variables, comprising both distributional and structural design variables. The method comprises a new augmented PDD of a high-dimensional stochastic response for statistical moment and reliability analyses; an integration of the augmented PDD, score functions, and finite-difference approximation for calculating the sensitivities of the first two moments and the failure probability with respect to distributional and structural design variables; and standard gradient-based optimization algorithms, leading to a multi-point, single-step design process. The innovative formulations of statistical moment and reliability analysis, design sensitivity analysis, and optimization algorithms have achieved not only highly accurate but also computationally efficient design solutions. Therefore, these new methods are capable of performing industrial-scale design optimization with numerous design variables.

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Winkelmann, Beate Maria. "Finite dimensional optimization methods and their application to optimal control with PDE constraints /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2005. http://wwwlib.umi.com/cr/ucsd/fullcit?p3205376.

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Opdahl, Hanna Belle. "Investigation of IsoTruss® Structures in Compression Using Numerical, Dimensional, and Optimization Methods." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/9243.

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The purpose of this research is to investigate the structural efficiency of 8-node IsoTruss structures subject to uniaxial compression using numerical, dimensional, and optimization methods. The structures analyzed herein are based on graphite/epoxy specimens that were designed for light-weight space applications, and are approximately 10 ft. (3 m) long and 0.3 lb. (0.14 kg). The principal failure modes considered are material failure, global buckling, local buckling at the bay level, and longitudinal strut buckling. Studies were performed with the following objectives: to correlate finite element predictions with experimental and analytical methods; to derive analytical expressions to predict bay-level buckling; to characterize interrelations between design parameters and buckling behavior; to develop efficient optimization methods; and, to compare the structural efficiency of outer longitudinal configurations with inner longitudinal configurations. Finite element models were developed in ANSYS, validated with experimental data, and verified with traditional mechanics. Data produced from the finite element models were used to identify trends between non-dimensional Pi variables, derived with Buckingham's Pi Theorem. Analytical expressions were derived to predict bay-level buckling loads, and verified with dimensional analyses. Numerical and dimensional analyses were performed on IsoTruss structures with outer longitudinal members to compare the structural performance with inner longitudinal configurations. Analytical expressions were implemented in optimization studies to determine efficient and robust optimization techniques and optimize the inner and outer longitudinal configurations with respect to mass. Results indicate that the finite element predictions of axial stiffness and global buckling loads correlate with traditional mechanics equations, but overestimate the capacity demonstrated in previously published experimental results. The buckling modes predicted by finite element predictions correlate with traditional mechanics and experimental results, except when the local and global buckling loads coincide. The analytical expressions derived from mechanics to predict local buckling underestimate the constraining influence of the helical members, and therefore underestimate the local buckling capacity. The optimization analysis indicates that, in the specified design space, the structure with outer longitudinal members demonstrates a greater strength-to-weight ratio than the corresponding structure with inner longitudinal members by sustaining the same loading criteria with 10% less mass.

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Acevedo, Feliz Daniel. "A framework for the perceptual optimization of multivalued multilayered two-dimensional scientific visualization methods." View abstract/electronic edition; access limited to Brown University users, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3318287.

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Schäfer, Christian. "Monte Carlo methods for sampling high-dimensional binary vectors." Phd thesis, Université Paris Dauphine - Paris IX, 2012. http://tel.archives-ouvertes.fr/tel-00767163.

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This thesis is concerned with Monte Carlo methods for sampling high-dimensional binary vectors from complex distributions of interest. If the state space is too large for exhaustive enumeration, these methods provide a mean of estimating the expected value with respect to some function of interest. Standard approaches are mostly based on random walk type Markov chain Monte Carlo, where the equilibrium distribution of the chain is the distribution of interest and its ergodic mean converges to the expected value. We propose a novel sampling algorithm based on sequential Monte Carlo methodology which copes well with multi-modal problems by virtue of an annealing schedule. The performance of the proposed sequential Monte Carlo sampler depends on the ability to sample proposals from auxiliary distributions which are, in a certain sense, close to the current distribution of interest. The core work of this thesis discusses strategies to construct parametric families for sampling binary vectors with dependencies. The usefulness of this approach is demonstrated in the context of Bayesian variable selection and combinatorial optimization of pseudo-Boolean objective functions.

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Yi, Congrui. "Penalized methods and algorithms for high-dimensional regression in the presence of heterogeneity." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2299.

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In fields such as statistics, economics and biology, heterogeneity is an important topic concerning validity of data inference and discovery of hidden patterns. This thesis focuses on penalized methods for regression analysis with the presence of heterogeneity in a potentially high-dimensional setting. Two possible strategies to deal with heterogeneity are: robust regression methods that provide heterogeneity-resistant coefficient estimation, and direct detection of heterogeneity while estimating coefficients accurately in the meantime. We consider the first strategy for two robust regression methods, Huber loss regression and quantile regression with Lasso or Elastic-Net penalties, which have been studied theoretically but lack efficient algorithms. We propose a new algorithm Semismooth Newton Coordinate Descent to solve them. The algorithm is a novel combination of Semismooth Newton Algorithm and Coordinate Descent that applies to penalized optimization problems with both nonsmooth loss and nonsmooth penalty. We prove its convergence properties, and show its computational efficiency through numerical studies. We also propose a nonconvex penalized regression method, Heterogeneity Discovery Regression (HDR) , as a realization of the second idea. We establish theoretical results that guarantees statistical precision for any local optimum of the objective function with high probability. We also compare the numerical performances of HDR with competitors including Huber loss regression, quantile regression and least squares through simulation studies and a real data example. In these experiments, HDR methods are able to detect heterogeneity accurately, and also largely outperform the competitors in terms of coefficient estimation and variable selection.

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Yan, Enxu. "Sublinear-Time Learning and Inference for High-Dimensional Models." Research Showcase @ CMU, 2018. http://repository.cmu.edu/dissertations/1207.

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Across domains, the scale of data and complexity of models have both been increasing greatly in the recent years. For many models of interest, tractable learning and inference without access to expensive computational resources have become challenging. In this thesis, we approach efficient learning and inference through the leverage of sparse structures inherent in the learning objective, which allows us to develop algorithms sublinear in the size of parameters without compromising the accuracy of models. In particular, we address the following three questions for each problem of interest: (a) how to formulate model estimation as an optimization problem with tractable sparse structure, (b) how to efficiently, i.e. in sublinear time, search, maintain, and utilize the sparse structures during training and inference, (c) how to guarantee fast convergence of our optimization algorithm despite its greedy nature? By answering these questions, we develop state-of-the-art algorithms in varied domains. Specifically, in the extreme classification domain, we utilizes primal and dual sparse structures to develop greedy algorithms of complexity sublinear in the number of classes, which obtain state-of-the-art accuracies on several benchmark data sets with one or two orders of magnitude speedup over existing algorithms. We also apply the primal-dual-sparse theory to develop a state-of-the-art trimming algorithm for Deep Neural Networks, which sparsifies neuron connections of a DNN with a task-dependent theoretical guarantee, which results in models of smaller storage cost and faster inference speed. When it comes to structured prediction problems (i.e. graphical models) with inter-dependent outputs, we propose decomposition methods that exploit sparse messages to decompose a structured learning problem of large output domains into factorwise learning modules amenable to sublineartime optimization methods, leading to practically much faster alternatives to existing learning algorithms. The decomposition technique is especially effective when combined with search data structures, such as those for Maximum Inner-Product Search (MIPS), to improve the learning efficiency jointly. Last but not the least, we design novel convex estimators for a latent-variable model by reparameterizing it as a solution of sparse support in an exponentially high-dimensional space, and approximate it with a greedy algorithm, which yields the first polynomial-time approximation method for the Latent-Feature Models and Generalized Mixed Regression without restrictive data assumptions.

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Nikram, Elham. "Three essays on game theory and computation." Thesis, University of Exeter, 2016. http://hdl.handle.net/10871/28755.

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The results section of my thesis includes three chapters. The first two chapters are on theoretical game theory. In both chapters, by mathematical modelling and game theoretical tools, I am predicting the behaviour of the players in some real world issues. Hoteling-Downs model plays an important role in the modern political interpretations. The first chapter of this study investigates an extension of Hoteling-Downs model to have multi-dimensional strategy space and asymmetric candidates. Chapter 3 looks into the inspection game where the inspections are not the same in the series of sequential inspections. By modelling the game as a series of recursive zero-sum games I find the optimal strategy of the players in the equilibrium. The forth chapter investigates direct optimization methods for large scale problems. Using Matlab implementations of Genetic and Nelder-Mead algorithms, I compare the efficiency and accuracy of the most famous direct optimization methods for unconstraint optimization problems based on differing number of variables.

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Книги з теми "Methods of three-dimensional optimization"

Catalano, L. A. Two dimensional optimization of smoothing properties of multistage schemes applied to hyperbolic equations. von Karman Institute for Fluid Dynamics, 1990.

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Facchinei, Francisco. Finite-Dimensional Variational Inequalities and Complementarity Problems. Springer-Verlag New York, Inc., 2004.

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Williams, David H. Airborne four-dimensional flight management in a time-based air traffic control environment. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1991.

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Baysal, Oktay. Efficient gradient-based shape optimization methodology using inviscid/viscous CFD: Summary of research report for the period of March 9, 1995 to March 8, 1997, grant# NCC-1-211. Dept. of Aerospace Engineering, College of Engineering and Technology, Old Dominion University, 1997.

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Glovackaya, Alevtina. Computational model. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1013723.

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The textbook covers the basics of classical numerical methods of computational mathematics used for solving linear and nonlinear equations and systems; interpolation and approximation of functions; numerical integration and differentiation; solutions of ordinary differential equations by methods of one-dimensional and multidimensional optimization. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is intended for students of higher educational institutions studying in the discipline "Numerical methods".

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Ohsaki, Makoto, and Makoto Ohsaki. Optimization of finite dimensional structures. Taylor & Francis, 2011.

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Optimization of finite dimensional structures. Taylor & Francis, 2011.

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Duck, Peter W. Three-dimensional marginal separation. Institute for Computational Mechanics in Propulsion, 1988.

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Three-dimensional echocardiography. Springer, 2011.

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Herman, Gabor T., and Joachim Frank, eds. Computational Methods for Three-Dimensional Microscopy Reconstruction. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-9521-5.

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Частини книг з теми "Methods of three-dimensional optimization"

Mishra, Shashi Kant, and Bhagwat Ram. "One-Dimensional Optimization Methods." In Introduction to Unconstrained Optimization with R. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0894-3_5.

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Laporte, Emmanuel, and Patrick Le Tallec. "Finite Dimensional Optimization." In Numerical Methods in Sensitivity Analysis and Shape Optimization. Birkhäuser Boston, 2003. http://dx.doi.org/10.1007/978-1-4612-0069-7_2.

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Qi, Liqun, and Defeng Sun. "Polyhedral Methods for Solving Three Index Assignment Problems." In Combinatorial Optimization. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3155-2_5.

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Rousselet, B. "Introduction to Shape Sensitivity Three-Dimensional and Surface Systems." In Shape and Layout Optimization of Structural Systems and Optimality Criteria Methods. Springer Vienna, 1992. http://dx.doi.org/10.1007/978-3-7091-2788-9_13.

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Cherkaev, Andrej. "Relaxation of One-Dimensional Variational Problems." In Variational Methods for Structural Optimization. Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1188-4_1.

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Andrei, Neculai. "Three-Term Conjugate Gradient Methods." In Nonlinear Conjugate Gradient Methods for Unconstrained Optimization. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42950-8_9.

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Stanimirović, Ivan. "Three Direct Methods in Linear Programming." In Advances in Optimization and Linear Programming. Apple Academic Press, 2021. http://dx.doi.org/10.1201/9781003256052-3.

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Meng, Jianjun, Zeqing Yang, and Zhenrui Peng. "Path Optimization of Large-Scale Automated Three-Dimensional Garage Based on Ant Colony Algorithm." In Computational Methods in Engineering & Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-48260-4_149.

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Hamdi, Mohsen, and Ayech Benjeddou. "Robust Multi-objective Evolutionary Optimization-Based Inverse Identification of Three-Dimensional Elastic Behaviour of Multilayer Unidirectional Fibre Composites." In Computational Methods in Applied Sciences. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44507-6_14.

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Filomeno Coelho, Rajan, Manyu Xiao, Aurore Guglielmetti, Manuel Herrera, and Weihong Zhang. "Investigation of Three Genotypes for Mixed Variable Evolutionary Optimization." In Computational Methods in Applied Sciences. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11541-2_20.

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Тези доповідей конференцій з теми "Methods of three-dimensional optimization"

Takahashi, Youhei, Satoshi Hasegawa, and Yoshio Hayasaki. "Second-harmonic optimization method of a hologram." In Digital Holography and Three-Dimensional Imaging. OSA, 2010. http://dx.doi.org/10.1364/dh.2010.dmb4.

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Yang, Dong-Yol, Seok-Ryul Lee, and Kyung-Jin Kim. "Design Optimization for Three-Dimensional Extrusion Processes." In MATERIALS PROCESSING AND DESIGN; Modeling, Simulation and Applications; NUMIFORM '07; Proceedings of the 9th International Conference on Numerical Methods in Industrial Forming Processes. AIP, 2007. http://dx.doi.org/10.1063/1.2740824.

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James, Kai, and Joaquim R. R. A. Martins. "Three-Dimensional Structural Topology Optimization of an Aircraft Wing Using Level Set Methods." In 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-6081.

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Galerkin, Y. B., A. A. Drozdov, and O. A. Solovyeva. "Centrifugal compressor stage impeller optimization by one-dimensional, quasi-three-dimensional and CFD methods." In INTERNATIONAL CONFERENCE ON SCIENCE AND APPLIED SCIENCE (ICSAS2020). AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0026722.

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Yildirim, Ali, and Sinan Eyi. "Three Dimensional Design Optimization Using Adjoint Method." In 34th AIAA Applied Aerodynamics Conference. American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-3868.

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Cao, Ling, and Qi-Peng Cao. "Genetic optimization for three-dimensional wing design method." In 37th Aerospace Sciences Meeting and Exhibit. American Institute of Aeronautics and Astronautics, 1999. http://dx.doi.org/10.2514/6.1999-400.

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YAMAZAKI, KOETSU, JIRO SAKAMOTO, and MASAMI KITANO. "THREE-DIMENSIONAL SHAPE OPTIMIZATION USING BOUNDARY ELEMENT METHOD." In 34th Structures, Structural Dynamics and Materials Conference. American Institute of Aeronautics and Astronautics, 1993. http://dx.doi.org/10.2514/6.1993-1642.

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Koc, Salim, Hyoung Kim, and Kazuhiro Nakahashi. "A Geometry Parameterization Method for Three-Dimensional Aerodynamic Optimization." In 34th AIAA Fluid Dynamics Conference and Exhibit. American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-2328.

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Acharjee, Swagato. "A gradient optimization method for efficient design of three-dimensional deformation processes." In MATERIALS PROCESSING AND DESIGN: Modeling, Simulation and Applications - NUMIFORM 2004 - Proceedings of the 8th International Conference on Numerical Methods in Industrial Forming Processes. AIP, 2004. http://dx.doi.org/10.1063/1.1766840.

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Ka¨mmerer, S., J. F. Mayer, M. Paffrath, U. Wever, and A. R. Jung. "Three-Dimensional Optimization of Turbomachinery Bladings Using Sensitivity Analysis." In ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38037.

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This paper presents an approach to optimize the geometry of turbomachinery blades utilizing an automated optimization loop. Optimization examples of turbomachinery blade geometries with selected objective functions and a set of design variables are introduced. The presented optimization examples are performed for a 1.5-stage turbine test case. The blade geometries are fully parameterized, enabling three-dimensional changes to the blade shape during the optimization. Therefore various non-physical and physical parameters such as stacking line or stagger angle can be selected as design variables. Three-dimensional steady-state numerical flow simulations and a sensitivity equation method are part of the optimization process. The design sensitivities used within the optimization are obtained by numerically solving the analytical sensitivity equations. This optimization scheme uses the same numerical method for the flow simulation and for the computation of the sensitivity equation. A Navier-Stokes flow solver, which has especially been designed for turbomachinery applications was used for the implementation of the sensitivity equation method. The focus of this paper is on the application of the described optimization strategy to turbomachinery flows. The presented optimization examples are used to demonstrate and to discuss the capabilities of this approach.

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Звіти організацій з теми "Methods of three-dimensional optimization"

Williamson, Chatt C., and Yansen Wang. Optimization of a Three-Dimensional Diagnostic Flow Solver. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada564452.

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Zabarankin, Michael. Semi-Analytical Approach to Three-Dimensional Shape Optimization Problems. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada482147.

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Cao, Yusong, William W. Schultz, and Robert F. Beck. Three-Dimensional Desingularized Boundary Integral Methods for Potential Problems. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada251151.

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Lewis, E. E. Variational nodal transport methods for hexagonal and three-dimensional geometries. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/7152709.

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Ruelas, Samantha. Optimization of PDMS Photoresin for Three-Dimensional Printng via Projection Micro-Stereolithography. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1460080.

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Chun, Kee S. Mathematical Methods of Three-Dimensional Eye Rotations Based Upon Spacecraft Dynamics Notation. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada361385.

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Lewis, E. E. Variational nodal transport methods for hexagonal and three-dimensional geometries. Final report. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10187641.

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Kinney, J. H., and D. L. Haupt. Three-dimensional x-ray tomography of crack-resistant composites: New paradigms for process optimization. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/105126.

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Bethel, E. Wes, and E. Wes Bethel. Exploration of Optimization Options for Increasing Performance of a GPU Implementation of a Three-dimensional Bilateral Filter. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1082192.

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Semerikov, Serhiy O., Illia O. Teplytskyi, Yuliia V. Yechkalo, and Arnold E. Kiv. Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. [б. в.], 2018. http://dx.doi.org/10.31812/123456789/2648.

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The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment without add-ins and macros. After analyzing a collection of writings of 1890-1950, the research determines the role of the scientific journal “Bulletin of Mathematical Biophysics”, its founder Nicolas Rashevsky and the scientific community around the journal in creating and developing models and methods of computational neuroscience. There are identified psychophysical basics of creating neural networks, mathematical foundations of neural computing and methods of neuroengineering (image recognition, in particular). The role of Walter Pitts in combining the descriptive and quantitative theories of training is discussed. It is shown that to acquire neural simulation competences in the spreadsheet environment, one should master the models based on the historical and genetic approach. It is indicated that there are three groups of models, which are promising in terms of developing corresponding methods – the continuous two-factor model of Rashevsky, the discrete model of McCulloch and Pitts, and the discrete-continuous models of Householder and Landahl.

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