Many today’s engineering tasks use approximation of their expensive objective function. Surrogate models, which are frequently used for this purpose, can save significant costs by substituting some of the experimental evaluations or simulations needed to achieve an optimal or near-optimal solution. This paper presents a surrogate model based on RBF networks. In contrast to the most of the surro...

An essential task for operation and planning of biogas plants is the optimization of substrate feed mixtures. Optimizing the monetary gain requires the determination of the exact amounts of maize, manure, grass silage, and other substrates. For this purpose, accurate simulation models are mandatory, because the underlying biochemical processes are very slow. The simulation models may be time-co...

Surrogate models are used to map input data output when the actual relationship between two is unknown or computationally expensive evaluate. Many techniques exist for surrogate modeling; however, selecting suitable a given application remains an open challenge. This work describes PRESTO, Random Forest classifier-based tool, recommend appropriate modeling dataset surface approximation and surr...

The paper deals with the application of evolutionary algorithms to black-box optimization, frequently encountered in biology, chemistry and engineering. In those areas, however, the evaluation of the black-box fitness is often costly and time-consuming. Such a situation is usually tackled by evaluating the original fitness only sometimes, and evaluating its appropriate response-surface model ot...

Abstract Surrogate models are commonly used to reduce the number of required expensive fitness evaluations in optimizing computationally problems. Although many competitive surrogate-assisted evolutionary algorithms have been proposed, it remains a challenging issue develop an effective model management strategy address problems with different landscape features under limited computational budg...

Integrating data-driven surrogate models and simulation models of di erent accuracies (or delities) in a single algorithm to address computationally expensive global optimization problems has recently attracted considerable attention. However, handling discrepancies between simulation models with multiple delities in global optimization is a major challenge. To address it, the two major contrib...

Real-time visualizations of drug pharmacokinetics and pharmacodynamics may help anesthesiologists more accurately titrate intravenous anesthetics for sedation and analgesia in a critical care setting. To assess synergism between propofol and opioids, our laboratory has developed response surface pharmacodynamic interaction models for remifentanil and propofol. These models use surrogate measure...

The use of surrogate models is a standard method to deal with complex, realworld optimization problems. The first surrogate models were applied to continuous optimization problems. In recent years, surrogate models gained importance for discrete optimization problems. This article, which consists of three parts, takes care of this development. The first part presents a survey of modelbased meth...

Uncertainty quantification analyses often employ surrogate models as computationally efficient approximations of computer codes simulating the physical phenomena. The accuracy and economy in the construction of surrogate models depends on the quality and quantity of data collected from the computationally expensive system models. Computationally efficient methods for accurate surrogate model tr...