[1]
L.W. Friedman and I. Pressman. The Metamodel in Simulation Analysis: Can It Be Trusted ?, In: The Journal of the Operational Research Society 39(10) (1988), pp.939-948.
DOI: 10.1057/jors.1988.160
Google Scholar
[2]
B. Yu and K. Popplewell. Metamodels in manufacturing: a review,. In: International Journal of Production Research 32(4) (1994), pp.787-796.
DOI: 10.1080/00207549408956970
Google Scholar
[3]
L. Baghdasaryan et al. Model Validation Via Uncertainty Propagation Using Response Surface Models,. In: Detc2002/dac-34140. 2002, pp.1-12.
Google Scholar
[4]
T.T. Do, L. Fourment, and M. Laroussi. Sensitivity Analysis and Optimization Al- gorithms for 3D Forging Process Design,. In: AIP Conference Proceedings 712. 2004, p.2026-(2031).
Google Scholar
[5]
H. Wiebenga, A.H. Boogaard, and G. Klaseboer. Sequential robust optimization of a V bending process using numerical simulations,. In: Structural and Multidisciplinary Opti- mization 46 (2012), pp.137-156.
DOI: 10.1007/s00158-012-0761-0
Google Scholar
[6]
R. Hino, F. Yoshida, and V.V. Toropov. Optimum blank design for sheet metal forming based on the interaction of high- and low-fidelity FE models,. In: Archive of Applied Mechanics 75 (2006), pp.679-691.
DOI: 10.1007/s00419-006-0047-3
Google Scholar
[7]
D. Huang et al. Sequential kriging optimization using multiple-fidelity evaluations,. In: Structural and Multidisciplinary Optimization 32(5) (2006), pp.369-382.
DOI: 10.1007/s00158-005-0587-0
Google Scholar
[8]
G. Sun et al. Multi-fidelity optimization for sheet metal forming process,. In: Structural and Multidisciplinary Optimization 44(1) (2010), pp.111-124.
DOI: 10.1007/s00158-010-0596-5
Google Scholar
[9]
Z. Qian et al. Building Surrogate Models Based on Detailed and Approximate Simula- tions,. In: ASME Journal of Mechanical Design 128 (2006), pp.668-677.
Google Scholar
[10]
E. Roux and P.O. Bouchard. Kriging metamodel global optimization of clinching joining processes accounting for ductile damage,. In: Journal of Materials Processing Technology 213 (2013), pp.10387-1047.
DOI: 10.1016/j.jmatprotec.2013.01.018
Google Scholar
[11]
B.M. Colosimo, L. Pagani, and M. Strano. Metamodeling Based on the Fusion of FEM Simulations Results and Experimental Data,. In: Key Engineering Materials. Vol. 554 - 557. 2013, pp.2487-2498.
DOI: 10.4028/www.scientific.net/kem.554-557.2487
Google Scholar
[12]
M. Strano, V. Mussi, and M. Monno. Non-conventional technologies for the manufac- turing of anti-intrusion bars,. In: International Journal of Material Forming 3 (2010), pp.1111-1114.
DOI: 10.1007/s12289-010-0966-y
Google Scholar
[13]
S.C.K. Yuen and G.N. Nurick. The Energy-Absorbing Characteristics of Tubular Struc- tures With Geometric and Material Modifications: An Overview,. In: Transactions of the ASME: Applied Mechanics Reviews 61 (2008), pp.1-15.
DOI: 10.1115/1.2885138
Google Scholar
[14]
T.J. Santner, B.J. Williams, and W.I. Notz. The Design and Analysis of Computer Ex- periments. Springer Verlag, (2003).
Google Scholar
[15]
M. L. Stein. Interpolation of Spatial Data: Some Theory for Kriging. Springer Series in Statistics, (1999).
Google Scholar
[16]
D.A. Harville. Maximum likelihood approaches to variance component estimation and to related problems,. In: Journal of the American Statistical Association 72 (1977), pp.320-338.
DOI: 10.1080/01621459.1977.10480998
Google Scholar
[17]
O. Shabenberger and C.A. Gotway. Statistical Methods for Spatial Data Analysis. Chap- man and Hall/CRC, (2005).
Google Scholar
[18]
M.C. Kennedy and A. O'Hagan. Predicting the Output from a Complex Computer Code When Fast Apporximations Are Available,. In: Biometrika 87 (2000), pp.1-13.
DOI: 10.1093/biomet/87.1.1
Google Scholar
[19]
L. Pagani. Multisensor Data Fusion for Quality Inspection of Free-Form Surfaces,. MA thesis. Politecnico di Milano, (2011).
Google Scholar