Metamodeling for High Dimensional Simulation-Based Design Problems

Computational tools such as finite element analysis and simulation are widely used in engineering, but they are mostly used for design analysis and validation. If these tools can be integrated for design optimization, it will undoubtedly enhance a manufacturer's competitiveness. Such integration, however, faces three main challenges: (1) high computational expense of simulation, (2) the simulation process being a black-box function, and (3) design problems being high dimensional. In the past two decades, metamodeling has been intensively developed to deal with expensive black-box functions, and has achieved success for low dimensional design problems. But when high dimensionality is also present in design, which is often found in practice, there lacks of a practical method to deal with the so-called high dimensional, expensive, and black-box (HEB) problems. This paper proposes the first metamodel of its kind to tackle the HEB problem. This paper integrates the radial basis function with high dimensional model representation into a new model, RBF-HDMR. The developed RBF-HDMR model offers an explicit function expression, and can reveal (1 ) the contribution of each design variable, (2) inherent linearity/nonlinearity with respect to input variables, and (3) correlation relationships among input variables. An accompanying algorithm to construct the RBF - HDMR has also been developed. The model and the algorithm fundamentally change the exponentially growing computation cost to be polynomial. Testing and comparison confirm the efficiency and capability of RBF-HDMR for HEB problems.

[1]  J. Friedman,et al.  Projection Pursuit Regression , 1981 .

[2]  J. Friedman Multivariate adaptive regression splines , 1990 .

[3]  Henry P. Wynn,et al.  Screening, predicting, and computer experiments , 1992 .

[4]  Ilya M. Sobol,et al.  Sensitivity Estimates for Nonlinear Mathematical Models , 1993 .

[5]  Eva Riccomagno,et al.  Experimental Design and Observation for Large Systems , 1996, Journal of the Royal Statistical Society: Series B (Methodological).

[6]  A. J. Booker,et al.  A rigorous framework for optimization of expensive functions by surrogates , 1998 .

[7]  H. Rabitz,et al.  An efficient chemical kinetics solver using high dimensional model representation , 1999 .

[8]  H. Rabitz,et al.  General foundations of high‐dimensional model representations , 1999 .

[9]  A. Owen Assessing linearity in high dimensions , 1999 .

[10]  Farrokh Mistree,et al.  Statistical Approximations for Multidisciplinary Design Optimization: The Problem of Size , 1999 .

[11]  T. Simpson,et al.  Comparative studies of metamodeling techniques under multiple modeling criteria , 2000 .

[12]  H. Rabitz,et al.  High Dimensional Model Representations , 2001 .

[13]  T. Simpson,et al.  Comparative studies of metamodelling techniques under multiple modelling criteria , 2001 .

[14]  H. Rabitz,et al.  High Dimensional Model Representations Generated from Low Dimensional Data Samples. I. mp-Cut-HDMR , 2001 .

[15]  Pierre Goovaerts,et al.  Adaptive Experimental Design Applied to Ergonomics Testing Procedure , 2002, DAC 2002.

[16]  Ruichen Jin,et al.  On Sequential Sampling for Global Metamodeling in Engineering Design , 2002, DAC 2002.

[17]  Donald R. Jones,et al.  Variable Screening in Metamodel Design by Cross-Validated Moving Least Squares Method , 2003 .

[18]  Ilya M. Sobol,et al.  Theorems and examples on high dimensional model representation , 2003, Reliab. Eng. Syst. Saf..

[19]  H. Rabitz,et al.  Random Sampling-High Dimensional Model Representation (RS-HDMR) with Nonuniformly Distributed Variables: Application to an Integrated Multimedia/ Multipathway Exposure and Dose Model for Trichloroethylene , 2003 .

[20]  G. G. Wang,et al.  Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points , 2003 .

[21]  Wei Chen,et al.  An Efficient Algorithm for Constructing Optimal Design of Computer Experiments , 2005, DAC 2003.

[22]  Yao Lin,et al.  An Efficient Robust Concept Exploration Method and Sequential Exploratory Experimental Design , 2004 .

[23]  Metin Demiralp,et al.  A factorized high dimensional model representation on the nodes of a finite hyperprismatic regular grid , 2005, Appl. Math. Comput..

[24]  Russell R. Barton,et al.  A review on design, modeling and applications of computer experiments , 2006 .

[25]  H. Rabitz,et al.  Random sampling-high dimensional model representation (RS-HDMR) and orthogonality of its different order component functions. , 2006, The journal of physical chemistry. A.

[26]  G. Gary Wang,et al.  Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007, DAC 2006.

[27]  M. Demiralp,et al.  Hybrid high dimensional model representation (HHDMR) on the partitioned data , 2006 .

[28]  Timothy W. Simpson,et al.  Design and Analysis of Computer Experiments in Multidisciplinary Design Optimization: A Review of How Far We Have Come - Or Not , 2008 .

[29]  B. Baxter,et al.  The Interpolation Theory of Radial Basis Functions , 2010, 1006.2443.

[30]  G. Gary Wang,et al.  Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions , 2010 .