We describe GTApprox—a new tool for medium-scale surrogate modeling in industrial
design. Compared to existing software, GTApprox brings several innovations: a few novel …

Global sensitivity analysis aims at quantifying respective effects of input random variables
(or combinations thereof) onto variance of a physical or mathematical model response …

Gaussian Processes are widely used for regression tasks. A known limitation in the
application of Gaussian Processes to regression tasks is that the computation of the solution …

In this paper, a new paradigm of user authentication called “wireless key” is described.
Following this concept, a novel many-to-many authentication scheme based on passive …

Engineers widely use Gaussian process regression framework to construct surrogate
models aimed to replace computationally expensive physical models while exploring design …

General predictive models do not provide a measure of confidence in predictions without
Bayesian assumptions. A way to circumvent potential restrictions is to use conformal …

This paper argues that there has not been enough discussion in the field of applications of
Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be …

We consider an unknown smooth function which maps high-dimensional inputs to
multidimensional outputs and whose domain of definition is an unknown low-dimensional …

We consider the problem of constructing predictive models (surrogate models) to tackle
challenges of industrial engineering design. The author analyzed the needs of industrial …

An appearance-based robot self-localization problem is considered in the machine learning
framework. The appearance space is composed of all possible images, which can be …