Preliminary design of a complex system often involves exploring a broad design space. This may require repeated use of computationally expensive simulations. To ease the computational burden, surrogate models are built to provide rapid approximations of more expensive models. However, the surrogate models themselves are often expensive to build because they are based on repeated experiments with computationally expensive simulations. An alternative approach is to replace the detailed simulations with simplified approximate simulations, thereby sacrificing accuracy for reduced computational time. Naturally, surrogate models built from these approximate simulations are also imprecise. A strategy is needed for improving the precision of surrogate models based on approximate simulations without significantly increasing computational time. In this paper, a new approach is taken to integrate data from approximate and detailed simulations to build a surrogate model that describes the relationship between output and input parameters. Experimental results from approximate simulations form the bulk of the data, and they are used to build a model based on a Gaussian process. The fitted model is then “adjusted” by incorporating a small amount of data from detailed simulations to obtain a more accurate prediction model. The effectiveness of this approach is demonstrated with a design example involving cellular materials for an electronics cooling application. The emphasis is on the method and not on the results per se.

Preliminary design of a complex system often involves exploring a large design space. This may require repeated use of computationally expensive simulations. To ease the computational burden, surrogate models are built to provide rapid approximations of more expensive models. However, the surrogate models themselves are often expensive to build because they are based on repeated experiments with computationally expensive simulations. An alternative approach is to replace the detailed simulations with simplified approximate simulations, thereby sacrificing accuracy for reduced computational time. Naturally, surrogate models built from these approximate simulations will also be imprecise. A strategy is needed for improving the precision of surrogate models based on approximate simulations without significantly increasing computational time. In this paper, a new approach is taken to integrate data from approximate and detailed simulations to build a surrogate model to describe the relationship between output and input parameters. Experimental results from approximate simulations form the bulk of the data, and they are used to build a model based on a Gaussian process. The fitted model is then ‘adjusted’ by incorporating small amounts of data from detailed simulations to obtain a more accurate prediction model. The effectiveness of this approach is demonstrated with a design application for a cellular material that is used to cool a microprocessor. The emphasis is on the method and not on the results per se.

Customer profile modelling is an essential element of marketing in service applications to aid understanding customer behaviour, designing customized service plans, and preventing churn activities. In many service applications, profile data are often generated that consist of customer transactions over time. This paper proposes using a functional mixture model to profile customer behaviour in order to identify and capture churn activity patterns. A five-step procedure is proposed based on the functional mixture model, which includes: (1) standardizing profiles, (2) screening out uninteresting profiles, (3) projecting profiles onto a feature space represented by a set of basis functions, (4) applying clustering algorithms to the resultant coefficients in the feature space, and (5) identifying interesting profiles. It is shown that the proposed framework is effective for detecting churn activities in the telecommunication industry. The method can also be easily generalized to model sophisticated manufacturing processes for quality improvement.

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