Abstract
In many cases, a design related simulation environment may be very large, com-prising many codes running in series, parallel, or both, each requiring anywhere from several seconds, to several hours for a single trial run. This may not be a practical or even feasible option when thousands of potential solutions have to be explored. Therefore, the use of a surrogate modeling is proposed to represent a modeling and simulation environment, and increase the efficiency of the exploration of alternatives and the quantification of their uncertainty many times fold. A surrogate model, also known as a metamodel, is a mathematical representation of a portion of a more complex or sophisticated analysis tool. It is generated based on a rigorous statistical regression of inputs and response metrics. These models allow for rapid calculation of responses by employing equations to relate independent variables to those responses. This chapter will cover two types of surrogate models polynomial Response Surface Equations (RSE), and Artificial Neural Networks (ANN).
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Notes
- 1.
A 2-level orthogonal array is an array that has two settings for each factor (the minimum and maximum values) and each case is uncorrelated with any of the other cases. Examples include some Taguchi arrays and some fractional factorial designs such as Plackett–Burman designs. If the number of factors is too large to efficiently find an orthogonal array, select a number of cases from the 2-level full factorial.
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Ender, T., Balestrini-Robinson, S. (2015). Surrogate Modeling. In: Loper, M. (eds) Modeling and Simulation in the Systems Engineering Life Cycle. Simulation Foundations, Methods and Applications. Springer, London. https://doi.org/10.1007/978-1-4471-5634-5_17
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DOI: https://doi.org/10.1007/978-1-4471-5634-5_17
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