Distance-based optimal sampling in a hypercube: Energy potentials for high-dimensional and low-saturation designs
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[1] Jan Eliáš,et al. Modification of the Maximin and ϕp (Phi) Criteria to Achieve Statistically Uniform Distribution of Sampling Points , 2020, Technometrics.
[2] John J. Borkowski,et al. Generation of space-filling uniform designs in unit hypercubes , 2012 .
[3] M. Stein. Large sample properties of simulations using latin hypercube sampling , 1987 .
[4] A. Owen. Controlling correlations in latin hypercube samples , 1994 .
[5] M. E. Johnson,et al. Minimax and maximin distance designs , 1990 .
[6] Eleni Chatzi,et al. Metamodeling of dynamic nonlinear structural systems through polynomial chaos NARX models , 2015 .
[7] J. Dick. Higher order scrambled digital nets achieve the optimal rate of the root mean square error for smooth integrands , 2010, 1007.0842.
[8] Jan Masek,et al. Parallel implementation of hyper-dimensional dynamical particle system on CUDA , 2018, Adv. Eng. Softw..
[9] Harald Niederreiter,et al. Random number generation and Quasi-Monte Carlo methods , 1992, CBMS-NSF regional conference series in applied mathematics.
[10] L. Pronzato. Minimax and maximin space-filling designs: some properties and methods for construction , 2017 .
[11] V. Roshan Joseph,et al. Space-filling designs for computer experiments: A review , 2016 .
[12] Yong Zhang,et al. Uniform Design: Theory and Application , 2000, Technometrics.
[13] K. Fang,et al. Number-theoretic methods in statistics , 1993 .
[14] Yunbao Huang,et al. Quasi-sparse response surface constructing accurately and robustly for efficient simulation based optimization , 2017, Adv. Eng. Softw..
[15] Avrim Blum,et al. Foundations of Data Science , 2020 .
[16] Arthur Flexer,et al. Choosing ℓp norms in high-dimensional spaces based on hub analysis , 2015, Neurocomputing.
[17] R. S. Anderssen,et al. Concerning $\int_0^1 \cdots \int_0^1 {(x_1^2 + \cdots + x_k^2 )} ^{{1 / 2}} dx_1 \cdots ,dx_k $ and a Taylor Series Method , 1976 .
[18] David H. Bailey,et al. Box integrals , 2007 .
[19] Art B. Owen,et al. Monte Carlo, Quasi-Monte Carlo, and Randomized Quasi-Monte Carlo , 2000 .
[20] Pierre L'Ecuyer,et al. Recent Advances in Randomized Quasi-Monte Carlo Methods , 2002 .
[21] Fred J. Hickernell,et al. A generalized discrepancy and quadrature error bound , 1998, Math. Comput..
[22] Art B. Owen,et al. Scrambling Sobol' and Niederreiter-Xing Points , 1998, J. Complex..
[23] F. Pillichshammer,et al. Digital Nets and Sequences: Discrepancy Theory and Quasi-Monte Carlo Integration , 2010 .
[24] Bruno Sudret,et al. Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model , 2015, Reliab. Eng. Syst. Saf..
[25] R. Cranley,et al. Randomization of Number Theoretic Methods for Multiple Integration , 1976 .
[26] Bruno Sudret,et al. Efficient design of experiments for sensitivity analysis based on polynomial chaos expansions , 2017, Annals of Mathematics and Artificial Intelligence.
[27] Chang-Xing Ma,et al. Wrap-Around L2-Discrepancy of Random Sampling, Latin Hypercube and Uniform Designs , 2001, J. Complex..
[28] Joe Wiart,et al. A new surrogate modeling technique combining Kriging and polynomial chaos expansions - Application to uncertainty analysis in computational dosimetry , 2015, J. Comput. Phys..
[29] Miroslav Vorechovský,et al. Modification of the Audze-Eglājs criterion to achieve a uniform distribution of sampling points , 2016, Adv. Eng. Softw..
[30] Charu C. Aggarwal,et al. On the Surprising Behavior of Distance Metrics in High Dimensional Spaces , 2001, ICDT.
[31] Filip De Turck,et al. Blind Kriging: Implementation and performance analysis , 2012, Adv. Eng. Softw..
[32] André T. Beck,et al. Performance of global metamodeling techniques in solution of structural reliability problems , 2017, Adv. Eng. Softw..
[33] Miroslav Vořechovský,et al. Distance-based optimal sampling in a hypercube: Analogies to N-body systems , 2019, Adv. Eng. Softw..
[34] KersaudyPierric,et al. A new surrogate modeling technique combining Kriging and polynomial chaos expansions - Application to uncertainty analysis in computational dosimetry , 2015 .
[35] Luc Pronzato,et al. Design of computer experiments: space filling and beyond , 2011, Statistics and Computing.
[36] A. Owen. Scrambled net variance for integrals of smooth functions , 1997 .
[37] F. J. Hickernell. Lattice rules: how well do they measure up? in random and quasi-random point sets , 1998 .
[38] FORMULATION OF POTENTIAL FOR DYNAMICAL PARTICLE SYSTEM APPLIED TO MONTE CARLO SAMPLING , 2017 .
[39] Thomas J. Santner,et al. Design and analysis of computer experiments , 1998 .
[40] R. Iman,et al. A distribution-free approach to inducing rank correlation among input variables , 1982 .
[41] T. J. Mitchell,et al. Exploratory designs for computational experiments , 1995 .
[42] William J. Welch,et al. Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization , 2006 .
[43] M. Vořechovský,et al. EVALUATION OF PAIRWISE DISTANCES AMONG POINTS FORMING A REGULAR ORTHOGONAL GRID IN A HYPERCUBE , 2018, Journal of Civil Engineering and Management.