Published in Arabian Journal of Geosciences
The infiltration rate is one of the primary processes of the hydrological cycle. It is the property of water by which it moves through the soil particles. Good knowledge of the infiltration rate is useful in calculating the natural and artificial groundwater recharge, soil erosion, and surface runoff. In this study, actual field measurements such a...
The design process of complex systems such as new configurations of aircraft or launch vehicles is usually decomposed in different phases which are characterized by the depth of the analyses in terms of number of design variables and fidelity of the physical models. At each phase, the designers have to deal with accurate but computationally intensi...
Published in Journal of the Brazilian Society of Mechanical Sciences and Engineering
The straightening process is the main cause of residual stresses in the manufacture of rails. It is a non-trivial process with cyclic plastic loads, solid–solid contact and complex geometry, which computational simulation is often complex and time-consuming. In this work, a new methodology was developed by means of a quasi-static modeling instead e...
Published in Proceedings of the National Academy of Sciences of the United States of America
The link between mind, brain, and behavior has mystified philosophers and scientists for millennia. Recent progress has been made by forming statistical associations between manifest variables of the brain (e.g., electroencephalogram [EEG], functional MRI [fMRI]) and manifest variables of behavior (e.g., response times, accuracy) through hierarchic...
We introduce a new variable selection method, called SgLasso, that handles extreme data, and suitable when the correlation between regressors is known. It is appropriate in genomics since once the genetic map has been built, the correlation is perfectly known. Besides, we prove that the signal to noise ratio is largely increased by considering the ...
Published in Biostatistics (Oxford, England)
Multi-dimensional functional data arises in numerous modern scientific experimental and observational studies. In this article, we focus on longitudinal functional data, a structured form of multidimensional functional data. Operating within a longitudinal functional framework we aim to capture low dimensional interpretable features. We propose a c...
Published in Statistics in medicine
Substantial advances in Bayesian methods for causal inference have been made in recent years. We provide an introduction to Bayesian inference for causal effects for practicing statisticians who have some familiarity with Bayesian models and would like an overview of what it can add to causal estimation in practical settings. In the paper, we demon...
Published in Materials
The calibration of any sophisticated model, and in particular a constitutive relation, is a complex problem that has a direct impact in the cost of generating experimental data and the accuracy of its prediction capacity. In this work, we address this common situation using a two-stage procedure. In order to evaluate the sensitivity of the model to...
Published in Biostatistics (Oxford, England)
Understanding the impact that environmental exposure during different stages of pregnancy has on the risk of adverse birth outcomes is vital for protection of the fetus and for the development of mechanistic explanations of exposure-disease relationships. As a result, statistical models to estimate critical windows of susceptibility have been devel...
Bayesian optimization methods have been successfully applied to black box optimization problems that are expensive to evaluate. In this paper, we adapt the so-called super efficient global optimization algorithm to solve more accurately mixed constrained problems. The proposed approach handles constraints by means of upper trust bound, the latter e...
