Isovolumetric adaptations to space-filling design of experiments
Tóm tắt
Từ khóa
Tài liệu tham khảo
Abdar M, Pourpanah F, Hussain S et al (2021) A review of uncertainty quantification in deep learning: techniques, applications and challenges. Inf Fus. https://doi.org/10.1016/j.inffus.2021.05.008
Aggarwal CC, Hinneburg A, Keim DA (2001) On the surprising behavior of distance metrics in high dimensional space. In: International conference on database theory, Springer, Uppsala, Sweden, pp 420–434
Alizadeh R, Allen J, Mistree F (2020) Managing computational complexity using surrogate models: a critical review. Res Eng Design 31:275–298. https://doi.org/10.1007/s00163-020-00336-7
Antinori G (2017) Uncertainty analysis and robust optimization for low pressure turbine rotors. PhD thesis, Technische Universität München, Munich, Germany, http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:91-diss-20160804-1279009-0-6
Audze P, Eglais V (1977) New approach to the design of experiments. Probl Dyn Strength 35:104–107
Beachkofski B, Grandhi R (2002) Improved distributed hypercube sampling. In: 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and materials conference, p 1274
Bhattacharyya B (2018) A critical appraisal of design of experiments for uncertainty quantification. Arch Computat Methods Eng 25(5):727–751. https://doi.org/10.1007/s11831-017-9211-x
Bouhlel MA, Hwang JT, Bartoli N et al (2019) A python surrogate modeling framework with derivatives. Adv Eng Soft. https://doi.org/10.1016/j.advengsoft.2019.03.005
Bratley P, Fox B (1988) Algorithm 659: implementing Sobol’s quasi-random sequence generator. ACM Trans Math Softw 14(1):88–100. https://doi.org/10.1145/42288.214372
Chen VC, Tsui KL, Barton RR et al (2006) A review on design, modeling and applications of computer experiments. IIE Trans 38(4):273–291. https://doi.org/10.1080/07408170500232495
Cioppa TM, Lucas TW (2007) Efficient nearly orthogonal and space-filling Latin hypercubes. Technometrics 49(1):45–55. https://doi.org/10.1198/004017006000000453
Crombecq K, Laermans E, Dhaene T (2011) Efficient space-filling and non-collapsing sequential design strategies for simulation-based modeling. Eur J Oper Res 214(3):683–696. https://doi.org/10.1016/j.ejor.2011.05.032
Damblin G, Couplet M, Iooss B (2013) Numerical studies of space-filling designs: optimization of Latin hypercube samples and subprojection properties. J Simul 7(4):276–289. https://doi.org/10.1057/jos.2013.16
De Rainville FM, Gagné C, Teytaud O et al (2012) Evolutionary optimization of low-discrepancy sequences. ACM Trans Model Comput Simul. doi 10(1145/2133390):2133393
Dussert C, Rasigni G, Rasigni M et al (1986) Minimal spanning tree: a new approach for studying order and disorder. Phys Rev B 34:3528–3531. https://doi.org/10.1103/PhysRevB.34.3528
Fang J, Sun G, Qiu N et al (2017) On design optimization for structural crashworthiness and its state of the art. Struct Multidisc Optim 55:1091–1119
Frank CP, Marlier RA, Pinon-Fischer OJ et al (2018) Evolutionary multi-objective multi-architecture design space exploration methodology. Optim Eng 19(2):359–381. https://doi.org/10.1007/s11081-018-9373-x
Garud SS, Karimi IA, Kraft M (2017) Design of computer experiments: a review. Comput Chem Eng 106:71–95. https://doi.org/10.1016/j.compchemeng.2017.05.010
Glasserman P (2013) Monte Carlo methods in financial engineering, vol 53. Springer Science & Business Media, chap Quasi-Monte Carlo. https://doi.org/10.1007/978-0-387-21617-1
Gnewuch M, Srivastav A, Winzen C (2009) Finding optimal volume subintervals with k points and calculating the star discrepancy are np-hard problems. J Complex 25(2):115–127. https://doi.org/10.1016/j.jco.2008.10.001
Harase S (2019) Comparison of Sobol’ sequences in financial applications. Monte Carlo Methods Appl 25(1):61–74. https://doi.org/10.1515/mcma-2019-2029
Hebbal A, Brevault L, Balesdent M et al (2021) Bayesian optimization using deep Gaussian processes with applications to aerospace system design. Optim Eng 22(1):321–361. https://doi.org/10.1007/s11081-020-09517-8
Hickernell F (1998) A generalized discrepancy and quadrature error bound. Math comput 67(221):299–322. https://doi.org/10.1090/S0025-5718-98-00894-1
Hou T, Nuyens D, Roels S et al (2019) Quasi-Monte Carlo based uncertainty analysis: sampling efficiency and error estimation in engineering applications. Reliab Eng Syst Safety 191(106):549. https://doi.org/10.1016/j.ress.2019.106549
Husslage BG, Rennen G, Van Dam ER et al (2011) Space-filling Latin hypercube designs for computer experiments. Optim Eng 12(4):611–630. https://doi.org/10.1007/s11081-010-9129-8
Jin R, Chen W, Sudjianto A (2005) An efficient algorithm for constructing optimal design of computer experiments. J Stat Plan Inference 134(1):268–287. https://doi.org/10.1016/j.jspi.2004.02.014
Joe S, Kuo FY (2003) Remark on algorithm 659: implementing Sobol’s quasirandom sequence generator. ACM Trans Math Softw 29(1):49–57. https://doi.org/10.1145/641876.641879
Joe S, Kuo FY (2008) Constructing Sobol sequences with better two-dimensional projections. SIAM J Sci Comput 30(5):2635–2654. https://doi.org/10.1137/070709359
Johnson ME, Moore LM, Ylvisaker D (1990) Minimax and maximin distance designs. J Sat Plan Inference 26(2):131–148. https://doi.org/10.1016/0378-3758(90)90122-B
Kucherenko S, Albrecht D, Saltelli A (2015) Exploring multi-dimensional spaces: a comparison of Latin hypercube and quasi Monte Carlo sampling techniques. preprint, arXiv:1505.02350
Lange VA, Fender J, Duddeck F (2018) Relaxing high-dimensional constraints in the direct solution space method for early phase development. Optim Eng 19(4):887–915. https://doi.org/10.1007/s11081-018-9381-x
L’Ecuyer P, Lemieux C (2002) Recent advances in randomized Quasi-Monte Carlo methods. Springer US, New York, pp 419–474. https://doi.org/10.1007/0-306-48102-2_20
Li W, Lu L, Xie X et al (2017) A novel extension algorithm for optimized Latin hypercube sampling. J Stat Comput Simul 87(13):2549–2559. https://doi.org/10.1080/00949655.2017.1340475
Liu L (2005) Could enough samples be more important than better designs for computer experiments? In: 38th Annual simulation symposium, IEEE, pp 107–115
Manteufel R (2001) Distributed hypercube sampling algorithm. In: 19th AIAA applied aerodynamics conference, p 1673
McKay M, Beckman R, Conover W (1979) Acomparisonof three methodsforselecting valuesofinputvariablesinthe analysisofoutputfrom acomputercode. Technometrics 21(2):239–245
Montgomery DC (2009) Design and analysis of experiments, 7th edn. Wiley, Hoboken
Morris MD, Mitchell TJ (1995) Exploratory designs for computational experiments. J Stat Plan Inference 43(3):381–402. https://doi.org/10.1016/0378-3758(94)00035-T
Niederreiter H (1992) Random number generation and quasi-Monte Carlo methods. SIAM, New Delhi. https://doi.org/10.1137/1.9781611970081
Pronzato L (2017) Minimax and maximin space-filling designs: some properties and methods for construction. J de la Société Française de Stat 158(1):7–36
Rajabi MM, Ataie-Ashtiani B, Janssen H (2015) Efficiency enhancement of optimized Latin hypercube sampling strategies: application to Monte Carlo uncertainty analysis and meta-modeling. Adv Water Resour 76:127–139. https://doi.org/10.1016/j.advwatres.2014.12.008
Verleysen M (2003) Learning high-dimensional data. Nato Sci Series Sub Series III Comput Syst Sci 186:141–162
Viana FAC (2013) Things you wanted to know about the Latin hypercube design and were afraid to ask. In: 10th World congress on structural and multidisciplinary optimization, Orlando, FL, USA
Viana FAC (2016) A tutorial on Latin hypercube design of experiments. Quality and Reliab Eng Int 32(5):1975–1985. https://doi.org/10.1002/qre.1924
Vořechovský M, Eliáš J (2020) Modification of the maximin and $$\phi _p$$ criteria to achieve statistically uniform distribution of sampling points. Technometrics 62(3):371–386. https://doi.org/10.1080/00401706.2019.1639550
Vořechovský M, Mašek J (2020) Distance-based optimal sampling in a hypercube: energy potentials for high-dimensional and low-saturation designs. Adv Eng Soft 149(102):880. https://doi.org/10.1016/j.advengsoft.2020.102880
Vořechovský M, Novák D (2009) Correlation control in small-sample Monte Carlo type simulations i: a simulated annealing approach. Probab Eng Mech 24(3):452–462. https://doi.org/10.1016/j.probengmech.2009.01.004
Wu Z, Wang D, Okolo PN et al (2017) Efficient space-filling and near-orthogonality sequential Latin hypercube for computer experiments. Comput Methods Appl Mech Eng 324:348–365. https://doi.org/10.1016/j.cma.2017.05.020
Wurm A, Bestle D (2016) Robust design optimization for improving automotive shift quality. Optim Eng 17(2):421–436. https://doi.org/10.1007/s11081-015-9290-1
Ye KQ (1998) Orthogonal column Latin hypercubes and their application in computer experiments. J Am Stat Assoc 93(444):1430–1439. https://doi.org/10.1080/01621459.1998.10473803
Ye KQ, Li W, Sudjianto A (2000) Algorithmic construction of optimal symmetric Latin hypercube designs. J stat plan Inference 90(1):145–159. https://doi.org/10.1016/S0378-3758(00)00105-1
Yondo R, Andrés E, Valero E (2018) A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses. Prog Arospace Sci 96:23–61