The fidelity of surrogate models remains one of the primary concerns in their application to represent complex system behavior. Appropriate sampling of training points is one of the primary factors affecting the fidelity of surrogate models. This paper investigates the relative advantage of probability-based uniform sampling over distance-based uniform sampling, in training surrogate models who...

A machine-learning-based framework for estimating the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (e.g., random forests, LASSO) to map a large set of inexpensively computed ‘error indicators’ (i.e., features) produced by the surrogate model at a given time instance to a prediction of...

We present an approach to uncertainty propagation in dynamic systems, exploiting information provided by related experimental results along with their models. The approach relies on a solution mapping technique to approximate mathematical models by polynomial surrogate models. We use these surrogate models to formulate prediction bounds in terms of polynomial optimizations. Recent results on po...

We present an approach to uncertainty propagation in dynamic systems, exploiting information provided by related experimental results along with their models. The approach relies on a solution mapping technique to approximate mathematical models by polynomial surrogate models. We use these surrogate models to formulate prediction bounds in terms of polynomial optimizations. Recent results on po...

When dealing with computationally expensive simulation codes or process measurement data, global surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization and optimization. Popular surrogate model types include neural networks, support vector machines, and splines. In addition, the cost of each simulation mandates the use...

Surrogate reasoning is reasoning whose task is partially taken over by operations on external aids, such as sentences, diagrams, physical models, mathematical models, and computers. Drawing on the basic concepts in situation theory, we present a semi-formal model of surrogate reasoning. We claim that the relative advantages and disadvantages of different forms of surrogate reasoning can be expl...

One important issue commonly encountered in the analysis of microarray data is to decide which and how many genes should be selected for further studies. For discriminant microarray data analyses based on statistical models, such as the logistic regression models, gene selection can be accomplished by a comparison of the maximum likelihood of the model given the real data, L(D|M), and the expec...

Using temperature and pressure records from Czech meteorological stations and NCEP=NCAR reanalysis series, we tested for the presence of detectable nonlinearity in univariate and multivariate climatic time series. The method of surrogate data was utilized for nonlinearity detection – results of nonlinear prediction for the original series were compared to the results for series whose nonlinear ...