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Relevant bibliographies by topics / Nested parameter spaces

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Academic literature on the topic 'Nested parameter spaces'

Author: Grafiati

Published: 23 August 2021

Last updated: 3 February 2022

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Journal articles on the topic "Nested parameter spaces"

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Pietrenko-Dabrowska, Anna, and Slawomir Koziel. "Low-Cost Surrogate Modeling of Miniaturized Microwave Components Using Nested Kriging." Applied Computational Electromagnetics Society 35, no. 11 (2021): 1346–47. http://dx.doi.org/10.47037/2020.aces.j.351142.

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In the paper, a recently reported nested kriging methodology is employed for modeling of miniaturized microwave components. The approach is based on identifying the parameter space region that contains high-quality designs, and, subsequently, rendering the surrogate in this subset. The results obtained for a miniaturized unequal-power-split rat-race coupler and a compact three-section impedance transformer demonstrate reliability of the method even for highly-dimensional parameter spaces, as well as its superiority over conventional modeling methods.

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Avrutin, Viktor, Bernd Eckstein, and Michael Schanz. "The bandcount increment scenario. III. Deformed structures." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 465, no. 2101 (2008): 41–57. http://dx.doi.org/10.1098/rspa.2008.0229.

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Bifurcation structures in two-dimensional parameter spaces formed by chaotic attractors alone are still a long way from being understood completely. In a series of three papers, we investigated the chaotic domain without periodic inclusions for a map, which is considered by many authors as some kind of one-dimensional canonical form for discontinuous maps. In Part I, the basic structures in the chaotic region are explained by the bandcount increment scenario. In Part II, fine self-similar substructures nested into the bandcount increment scenario are explained by the bandcount-adding and -doub

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Avrutin, Viktor, Bernd Eckstein, and Michael Schanz. "The bandcount increment scenario. II. Interior structures." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 464, no. 2097 (2008): 2247–63. http://dx.doi.org/10.1098/rspa.2007.0299.

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Bifurcation structures in the two-dimensional parameter spaces formed by chaotic attractors alone are still far away from being understood completely. In a series of three papers, we investigate the chaotic domain without periodic inclusions for a map, which is considered by many authors as some kind of one-dimensional canonical form for discontinuous maps. In this second part, we investigate fine substructures nested into the basic structures reported and explained in part I. It is demonstrated that the overall structure of the chaotic domain is caused by a complex interaction of bandcount in

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Koziel, Slawomir, and Anna Pietrenko-Dabrowska. "Reliable data-driven modeling of high-frequency structures by means of nested kriging with enhanced design of experiments." Engineering Computations 36, no. 7 (2019): 2293–308. http://dx.doi.org/10.1108/ec-02-2019-0054.

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Purpose A framework for reliable modeling of high-frequency structures by nested kriging with an improved sampling procedure is developed and extensively validated. A comprehensive benchmarking including conventional kriging and previously reported design of experiments technique is provided. The proposed technique is also demonstrated in solving parameter optimization task. Design/methodology/approach The keystone of the proposed approach is to focus the modeling process on a small region of the parameter space (constrained domain containing high-quality designs with respect to the selected p

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Wessing, Simon, and Manuel López-Ibáñez. "Latin Hypercube Designs with Branching and Nested Factors for Initialization of Automatic Algorithm Configuration." Evolutionary Computation 27, no. 1 (2019): 129–45. http://dx.doi.org/10.1162/evco_a_00241.

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The configuration of algorithms is a laborious and difficult process. Thus, it is advisable to automate this task by using appropriate automatic configuration methods. The [Formula: see text] method is among the most widely used in the literature. By default, [Formula: see text] initializes its search process via uniform sampling of algorithm configurations. Although better initialization methods exist in the literature, the mixed-variable (numerical and categorical) nature of typical parameter spaces and the presence of conditional parameters make most of the methods not applicable in practic

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Kim, Jeong-Gyoo. "The Hilbert Space of Double Fourier Coefficients for an Abstract Wiener Space." Mathematics 9, no. 4 (2021): 389. http://dx.doi.org/10.3390/math9040389.

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Fourier series is a well-established subject and widely applied in various fields. However, there is much less work on double Fourier coefficients in relation to spaces of general double sequences. We understand the space of double Fourier coefficients as an abstract space of sequences and examine relationships to spaces of general double sequences: p-power summable sequences for p = 1, 2, and the Hilbert space of double sequences. Using uniform convergence in the sense of a Cesàro mean, we verify the inclusion relationships between the four spaces of double sequences; they are nested as prope

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Karvonen, Toni, Chris Oates, and Mark Girolami. "Integration in reproducing kernel Hilbert spaces of Gaussian kernels." Mathematics of Computation 90, no. 331 (2021): 2209–33. http://dx.doi.org/10.1090/mcom/3659.

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The Gaussian kernel plays a central role in machine learning, uncertainty quantification and scattered data approximation, but has received relatively little attention from a numerical analysis standpoint. The basic problem of finding an algorithm for efficient numerical integration of functions reproduced by Gaussian kernels has not been fully solved. In this article we construct two classes of algorithms that use N N evaluations to integrate d d -variate functions reproduced by Gaussian kernels and prove the exponential or super-algebraic decay of their worst-case errors. In contrast to earl

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Koziel, Slawomir, and Anna Pietrenko-Dabrowska. "Rapid multi-objective optimization of antennas using nested kriging surrogates and single-fidelity EM simulation models." Engineering Computations 37, no. 4 (2019): 1491–512. http://dx.doi.org/10.1108/ec-05-2019-0200.

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Purpose This study aims to propose a computationally efficient framework for multi-objective optimization (MO) of antennas involving nested kriging modeling technology. The technique is demonstrated through a two-objective optimization of a planar Yagi antenna and three-objective design of a compact wideband antenna. Design/methodology/approach The keystone of the proposed approach is the usage of recently introduced nested kriging modeling for identifying the design space region containing the Pareto front and constructing fast surrogate model for the MO algorithm. Surrogate-assisted design r

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Nixon, Matthew C., and Nikku Madhusudhan. "Assessment of supervised machine learning for atmospheric retrieval of exoplanets." Monthly Notices of the Royal Astronomical Society 496, no. 1 (2020): 269–81. http://dx.doi.org/10.1093/mnras/staa1150.

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ABSTRACT Atmospheric retrieval of exoplanets from spectroscopic observations requires an extensive exploration of a highly degenerate and high-dimensional parameter space to accurately constrain atmospheric parameters. Retrieval methods commonly conduct Bayesian parameter estimation and statistical inference using sampling algorithms such as Markov chain Monte Carlo or Nested Sampling. Recently several attempts have been made to use machine learning algorithms either to complement or to replace fully Bayesian methods. While much progress has been made, these approaches are still at times unabl

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NISTOR, VICTOR, and CHRISTOPH SCHWAB. "HIGH-ORDER GALERKIN APPROXIMATIONS FOR PARAMETRIC SECOND-ORDER ELLIPTIC PARTIAL DIFFERENTIAL EQUATIONS." Mathematical Models and Methods in Applied Sciences 23, no. 09 (2013): 1729–60. http://dx.doi.org/10.1142/s0218202513500218.

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Let D ⊂ ℝd, d = 2, 3, be a bounded domain with piecewise smooth boundary, Y = ℓ∞(ℕ) and U = B1(Y), the open unit ball of Y. We consider a parametric family (Py)y∈U of uniformly strongly elliptic, second-order partial differential operators Py on D. Under suitable assumptions on the coefficients, we establish a regularity result for the solution u of the parametric boundary value problem Py u(x, y) = f(x, y), x ∈ D, y ∈ U, with mixed Dirichlet–Neumann boundary conditions on ∂d D and, respectively, on ∂n D. Our regularity and well-posedness results are formulated in a scale of weighted Sobolev s

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Dissertations / Theses on the topic "Nested parameter spaces"

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Higson, Edward John. "Bayesian methods and machine learning in astrophysics." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289728.

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This thesis is concerned with methods for Bayesian inference and their applications in astrophysics. We principally discuss two related themes: advances in nested sampling (Chapters 3 to 5), and Bayesian sparse reconstruction of signals from noisy data (Chapters 6 and 7). Nested sampling is a popular method for Bayesian computation which is widely used in astrophysics. Following the introduction and background material in Chapters 1 and 2, Chapter 3 analyses the sampling errors in nested sampling parameter estimation and presents a method for estimating them numerically for a single nested sam

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Hee, Sonke. "Computational Bayesian techniques applied to cosmology." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/273346.

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This thesis presents work around 3 themes: dark energy, gravitational waves and Bayesian inference. Both dark energy and gravitational wave physics are not yet well constrained. They present interesting challenges for Bayesian inference, which attempts to quantify our knowledge of the universe given our astrophysical data. A dark energy equation of state reconstruction analysis finds that the data favours the vacuum dark energy equation of state $w {=} -1$ model. Deviations from vacuum dark energy are shown to favour the super-negative ‘phantom’ dark energy regime of $w {< } -1$, but at low st

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Turčičová, Marie. "Odhad varianční matice pro filtraci ve vysoké dimenzi." Doctoral thesis, 2021. http://www.nusl.cz/ntk/nusl-449094.

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Estimating large covariance matrices from small samples is an important problem in many fields. Among others, this includes spatial statistics and data assimilation. In this thesis, we deal with several methods of covariance estimation with emphasis on regula- rization and covariance models useful in filtering problems. We prove several properties of estimators and propose a new filtering method. After a brief summary of basic esti- mating methods used in data assimilation, the attention is shifted to covariance models. We show a distinct type of hierarchy in nested models applied to the spect

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Book chapters on the topic "Nested parameter spaces"

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Patwari, Ashish. "Sparse Linear Antenna Arrays: A Review." In Antenna Systems [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.99444.

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Linear sparse antenna arrays have been widely studied in array processing literature. They belong to the general class of non-uniform linear arrays (NULAs). Sparse arrays need fewer sensor elements than uniform linear arrays (ULAs) to realize a given aperture. Alternately, for a given number of sensors, sparse arrays provide larger apertures and higher degrees of freedom than full arrays (ability to detect more source signals through direction-of-arrival (DOA) estimation). Another advantage of sparse arrays is that they are less affected by mutual coupling compared to ULAs. Different types of linear sparse arrays have been studied in the past. While minimum redundancy arrays (MRAs) and minimum hole arrays (MHAs) existed for more than five decades, other sparse arrays such as nested arrays, co-prime arrays and super-nested arrays have been introduced in the past decade. Subsequent to the introduction of co-prime and nested arrays in the past decade, many modifications, improvements and alternate sensor array configurations have been presented in the literature in the past five years (2015–2020). The use of sparse arrays in future communication systems is promising as they operate with little or no degradation in performance compared to ULAs. In this chapter, various linear sparse arrays have been compared with respect to parameters such as the aperture provided for a given number of sensors, ability to provide large hole-free co-arrays, higher degrees of freedom (DOFs), sharp angular resolutions and susceptibility to mutual coupling. The chapter concludes with a few recommendations and possible future research directions.

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Conference papers on the topic "Nested parameter spaces"

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Koziel, Slawomir, Anna Pietrenko-Dabrowska, Qingsha S. Cheng, and Zhen Zhang. "Low-Cost Surrogate Modeling of Compact Microstrip Circuits in Highly-Dimensional Parameters Spaces Using Variable-Fidelity Nested Co-Kriging." In 2020 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO). IEEE, 2020. http://dx.doi.org/10.1109/nemo49486.2020.9343603.

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Eckstein, Eugene C., Vinay Bhal, JoDe M. Lavine, Baoshun Ma, Mark Leggas, and Jerome A. Goldstein. "Nested First-Passages of Tracer Particles in Flows of Blood and Control Suspensions: Symmetry and Lorentzian Transformations." In ASME 2017 Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/fedsm2017-69549.

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Theory of molecular Taylor-Aris dispersion (TAD) is an accepted framework describing tracer dispersion in suspension flows and determining effective diffusion coefficients. Our group reported a pseudo-Lagrangian method to study dispersion in suspension flows at FEDSM-2000. Tracer motions were studied in a steadily moving inertial reference frame (SMIRF) aligned with the flow direction; increments of change of axial position of individual tracers were collected to demonstrate how the tracer moved as they, individually, interacted with similar collections of other bodies brought to and from the

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Kilchyk, Viktor, Emily Senay, and Ahmed Abdelwahab. "Selection of the Optimum Control Parameters for Compressor Design Optimization Algorithm." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-63009.

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Topology optimization in turbomachinery is a challenging nonlinear problem with a large number of variables. Compressor efficiency function is dependent on the particular design space and may complicate due to numerical (CFD) solution issue. This makes it difficult to provide high precision, fast convergence, and robustness that is required by a modern, fast paced manufacturing environment. Even the best optimization algorithms exhibit difficulties in addressing these issues. To resolve these difficulties, a new algorithm testing, and training approach was proposed. In the new approach, traini

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Huang, Dongli, and Hany S. Abdel-Khalik. "Development of Uncertainty Quantification Capability for NESTLE." In 2017 25th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/icone25-67797.

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This work aims to develop an uncertainty analysis methodology for the propagation and quantification of the effects of nuclear cross-section uncertainties on important core-wide attributes, such as power distribution and core critical eigenvalue. Given the computationally taxing nature of this endeavor, our goal is to develop a methodology capable of preserving the accuracy of brute force sampling techniques for uncertainty quantification while realizing the efficiency of deterministic techniques. To achieve that, a reduced order modeling (ROM) approach is proposed to deal with the enormous si

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Khetan, Ashish, and James T. Allison. "Large-Scale Topology Optimization Using Parameterized Boolean Networks." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34256.

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A novel parameterization concept for structural truss topology optimization is presented in this article that enables the use of evolutionary algorithms in design of large-scale structures. The representational power of Boolean networks is used here to parameterize truss topology. A genetic algorithm then operates on parameters that govern the generation of truss topologies using this random network instead of operating directly on design variables. A genetic algorithm implementation is also presented that is congruent with the local rule application of the random network. The primary advantag

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Hamel, J. M. "Cooperative Design Optimization (CDO) for Multidisciplinary Systems." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35299.

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Design engineers and decision-makers across various fields are constantly working to make optimal design decisions for multidisciplinary engineering systems in an effort to improve performance and reduce costs. The multiple disciplines that decision-makers are forced to consider can range from different physical components of a system, to competing physical phenomena influencing a component (e.g. flow forces and structural strength), to completely separate models of interest to a system (e.g. engineering performance and lifecycle cost). The common element that all these decision-making scenari

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Abdel-Khalik, Hany S., Dongli Huang, Ondrej Chvala, and G. Ivan Maldonado. "Towards Development of Uncertainty Library for Nuclear Reactor Core Simulation." In 2018 26th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/icone26-82385.

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Uncertainty quantification is an indispensable analysis for nuclear reactor simulation as it provides a rigorous approach by which the credibility of the predictions can be assessed. Focusing on propagation of multi-group cross-sections, the major challenge lies in the enormous size of the uncertainty space. Earlier work has explored the use of the physics-guided coverage mapping (PCM) methodology to assess the quality of the assumptions typically employed to reduce the size of the uncertainty space. A reduced order modeling (ROM) approach has been further developed to identify the active degr

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McHale, Ciarán, Robert Telford, and Paul M. Weaver. "Compact Telescopic Morphing Lattice Boom." In ASME 2019 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/smasis2019-5620.

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Abstract This paper reports on the design and manufacture of a compact telescopic morphing lattice (CTML) space boom. This boom stows within a 1U CubeSat volume and weighs only 0.475kg. Once deployed, the CTML has a total length of 2m, 20 times the stowed height. The device consists of three multi-stable cylindrical composite lattices connected in series. To improve packaging efficiency, these lattices nest inside one another in the stowed configuration. The morphing lattice is a structure that uses prestress and lamina orientation to seamlessly morph from a short stowed state to a long deploy

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Durocher, Antoine, Gilles Bourque, and Jeffrey M. Bergthorson. "Quantifying the Effect of Kinetic Uncertainties on NO Predictions at Engine-Relevant Pressures in Premixed Methane-Air Flames." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-90486.

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Abstract Accurate and robust thermochemical models are required to identify future low-NOx technologies that can meet the increasingly stringent emissions regulations in the gas turbine industry. These mechanisms are generally optimized and validated for specific ranges of operating conditions, which result in an abundance of models offering accurate nominal solutions over different parameter ranges. At atmospheric conditions, and for methane combustion, a relatively good agreement between models and experiments is currently observed. At engine-relevant pressures, however, a large variability

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