Typical challenges of simulation-based design optimization include unavailable gradients and unreliable approximations thereof, expensive function evaluations, numerical noise, multiple local optima and the failure of the analysis to return a value to the optimizer. One possible remedy to alleviate these issues is to use surrogate models in lieu of the computational models or simulations and de...

Large-scale computational models have become common tools for analyzing complex manmade systems. However, when coupled with optimization or uncertainty quantification methods in order to conduct extensive model exploration and analysis, the computational expense quickly becomes intractable. Furthermore, these models may have both continuous and discrete parameters. One common approach to mitiga...

This paper investigates the characterization of the uncertainty in the prediction of surrogate models. In the practice of engineering, where predictive models are pervasively used, the knowledge of the level of modeling error in any region of the design space is uniquely helpful for design exploration and model improvement. The lack of methods that can explore the spatial variation of surrogate...

We address a central problem in modeling, namely that of learning an algebraic model from data obtained from simulations or experiments. We propose a methodology that uses a small number of simulations or experiments to learn models that are as accurate and as simple as possible. The approach begins by building a low-complexity surrogate model. The model is built using a best subset technique t...

Ridge versions of an ill-conditioned system are alleged to ‘‘act more like an orthogonal system’’ than the system itself. Alternatives, called surrogates and based on the conditioning of linear systems, are shown to yield smaller expected mean squares than OLS, and uniformly smaller residual sums of squares than ridge. Ridge and surrogate solutions are compared on several marques of orthogonali...

Design mining is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping to meet given objectives. It enables the exploitation of novel materials and processes without formal models or complex simulation. In this article, we focus upon the coevolutionary nature of the design process whe...

The paper deals with surrogate modelling, a modern approach to the optimization of empirical objective functions. The approach leads to a substantial decrease of time and costs of evaluation of the objective function, a property that is particularly attractive in evolutionary optimization. In the paper, an extension of surrogate modelling with regression boosting is proposed. Such an extension ...

Automatic optimisers can play a vital role in the design and development of engineering systems and processes. However, a lack of available data to guide the search can result in the global optimum solution never being found. Surrogate models can be used to address this lack of data and allow more of the design space to be explored, as well as provide an overall computational saving. In this th...

Burkholderia pseudomallei is the Gram-negative bacterial pathogen responsible for the disease melioidosis. B. pseudomallei establishes disease in susceptible individuals through multiple routes of infection, all of which may proceed to a septicemic disease associated with a high mortality rate. B. pseudomallei opportunistically infects humans and a wide range of animals directly from the enviro...

Evaluation of failure probability of a given system requires sampling of the system response and can be computationally expensive. Therefore it is desirable to construct an accurate surrogate model for the system response and subsequently to sample the surrogate model. In this paper we discuss the properties of this approach. We demonstrate that the straightforward sampling of a surrogate model...