[1] Surrogate modeling, also called metamodeling, has evolved and been extensively used over the past decades. A wide variety of methods and tools have been introduced for surrogate modeling aiming to develop and utilize computationally more efficient surrogates of high-fidelity models mostly in optimization frameworks. This paper reviews, analyzes, and categorizes research efforts on surrogate...

Computational reliability assessment in mechanized tunneling requires realistic numerical models accounting for the uncertainty of geotechnical and tunneling process parameters. For real-time prognoses, surrogate models are in general inevitable to substitute expensive complex numerical simulations. In this paper, different strategies for surrogate modeling in mechanized tunneling simulations a...

In this work, we consider two kinds of model reduction techniques to simulate blood flow through the largest systemic arteries, where a stenosis is located in a peripheral artery i.e. in an artery that is located far away from the heart. For our simulations we place the stenosis in one of the tibial arteries belonging to the right lower leg (right post tibial artery). The model reduction techni...

This discussion aims to give a normative theoretical basis for a "best judgment" model of surrogate decision making rooted in a regulative ideal of love. Currently, there are two basic models of surrogate decision making for incompetent patients: the "substituted judgment" model and the "best interests" model. The former draws on the value of autonomy and responds with respect; the latter draws...

Many semantic search based on Genetic Programming (GP) use a trial-and-error scheme to attain semantically diverse offspring in the evolutionary search. This results in significant impediments on the success of semantic-based GP in solving real world problems, due to the additional computational overheads incurred. This paper proposes a surrogate Genetic Programming (or sGP in short) to retain ...

This paper discusses the benefits of different infill sampling criteria used in surrogate-model-based constrained global optimization. Here surrogate models are used to approximate both the objective and constraint functions with the assumption that these are computationally expensive to compute. The construction of these surrogates (also known as meta models or response surface models) involve...

This work presents an optimal model management strategy that exploits multifidelity surrogate models to accelerate the estimation of statistics of outputs of computationally expensive high-fidelity models. Existing acceleration methods typically exploit a multilevel hierarchy of surrogate models that follow a known rate of error decay and computational costs; however, a general collection of su...

One way to accelerate evolutionary algorithms with expensive fitness evaluations is to combine them with surrogate models. Surrogate models are efficiently computable approximations of the fitness function, derived by means of statistical or machine learning techniques from samples of fully evaluated solutions. But these models usually require a numerical representation, and therefore cannot be...

Abstract. In this paper, we propose a family of surrogate maximization (SM) algorithms for multi-class logistic regression models (also called conditional exponential models). An SM algorithm aims at turning an otherwise intractable maximization problem into a tractable one by iterating two steps. The S-step computes a tractable surrogate function to substitute the original objective function, ...