We describe an in situ system for solving iterative problems. We specifically target inverse problems, where expensive simulations are approximated using a surrogate model. The model explores the parameter space of the simulation through iterative trials, each of which becomes a job managed by a parallel scheduler. Our work extends Henson [1], a cooperative multi-tasking system for in situ exec...

Computational optimization holds great promise for the management of oil field operations. These optimizations can be very expensive computationally, however, because each function evaluation requires a reservoir simulation, which is itself time consuming. In this paper, we present and apply a surrogate modeling procedure which greatly accelerates optimizations based on subsurface flow simulati...

Using daily infection data for Hong Kong we explore the validity of a variety of models of disease propagation when applied to the SARS epidemic. Surrogate data methods show that simple random models are insufficient and that the standard epidemic susceptible-infected-removed model does not fully account for the underlying variability in the observed data. As an alternative, we consider a more ...

We study losses for binary classification and class probability estimation and extend the understanding of them from margin losses to general composite losses which are the composition of a proper loss with a link function. We characterise when margin losses can be proper composite losses, explicitly show how to determine a symmetric loss in full from half of one of its partial losses, introduc...

Insulin resistance contributes to the pathophysiology of diabetes, obesity, and their cardiovascular complications. Mouse models of these human diseases are useful for gaining insight into pathophysiological mechanisms. The reference standard for measuring insulin sensitivity in both humans and animals is the euglycemic glucose clamp. Many studies have compared surrogate indexes of insulin sens...

INTRODUCTION With the increase of wireless communication networks, people ask more and more information on the impact of electromagnetic field on their health. The assessment of the exposure to electromagnetic waves is the key question. Most of investigations of numerical dosimetry studies deal with a single plane wave exposure [1] but there is a lack of information on multiple waves exposure. ...

After the initialization, the sequential component of the optimization begins. At iteration t, all previously observed data y = y1:t at points X = X1:t is used to construct a probabilistic surrogate model s y,X. The next location xt+1 is determined by maximizing a chosen acquisition function which measures the benefit or utility associated with evaluating a proposed x 2 X . In this article, we ...

This paper presents a novel technique that uses metaheuristics and machine learning to automate the optimization of design parameters for reconfigurable designs. Traditionally, such an optimization involves manual application analysis as well as model and parameter space exploration tool creation. We develop a Machine Learning Optimizer (MLO) to automate this process. From a number of benchmark...

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 ...

In this paper, we consider the efficient and reliable solution of distributed optimal control problems governed by parametrized elliptic partial differential equations. The reduced basis method is used as a low-dimensional surrogate model to solve the optimal control problem. To this end, we introduce reduced basis spaces not only for the state and adjoint variable but also for the distributed ...