In the era of big data, analysts usually explore various statistical models or machine-learning
methods for observed data to facilitate scientific discoveries or gain predictive power …

The use of metamodeling techniques in the design and analysis of computer experiments
has progressed remarkably in the past 25 years, but how far has the field really come? This …

There is an increasing interest in creating high-resolution 3D subsurface geo-models using
multisource retrieved data, ie, borehole, geophysical techniques, geological maps, and rock …

Behavioral systems theory decouples the behavior of a system from its representation. A key
result is that, under a persistency of excitation condition, the image of a Hankel matrix …

Thyroid disease is the general concept for a medical problem that prevents one's thyroid
from producing enough hormones. Thyroid disease can affect everyone—men, women …

We conduct an extensive empirical study on short-term electricity price forecasting (EPF) to
address the long-standing question if the optimal model structure for EPF is univariate or …

In this book, we provide a comprehensive introduction to the most recent advances in the
application of machine learning methods in quantum sciences. We cover the use of deep …

A central problem in modeling, namely that of learning an algebraic model from data
obtained from simulations or experiments is addressed. A methodology that uses a small …

Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …

Baryonic feedback effects consist of a major systematic for upcoming weak-lensing and
galaxy-clustering surveys. In this paper, we present an emulator for the baryonic …