Metamodels for Computer-based Engineering Design: Survey and recommendations

. The use of statistical techniques,to build ap- proximations,of expensive computer,analysis codes pervades much,of today’s engineering,design. These statistical approxi- mations, or metamodels, are used to replace the actual expensive computer analyses, facilitating multidisciplinary, multiobjective optimization and,concept,exploration. In this paper, we review several of these techniques, including design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning and kriging. We survey their existing application in engineering design, and then address the dangers of applying traditional statistical techniques,to approximate,deterministic computer analysis codes. We conclude,with recommendations,for the appropriate,use,of statistical approximation,techniques,in given situations, and how common pitfalls can be avoided. Keywords.,Deterministic,analysis; Engineering