Over the past few years, Deep Learning (DL) methods have garnered substantial
recognition within the field of hydrology and water resources applications. Beginning with a …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

The increasing availability of satellite data at higher spatial, temporal and spectral
resolutions is enabling new applications in agriculture and economic development …

Deep learning (DL), a new generation of artificial neural network research, has transformed
industries, daily lives, and various scientific disciplines in recent years. DL represents …

Data science models, although successful in a number of commercial domains, have had
limited applicability in scientific problems involving complex physical phenomena. Theory …

Our ability to collect “big data” has greatly surpassed our capability to analyze it,
underscoring the emergence of the fourth paradigm of science, which is datadriven …

This review presents machine learning (ML) approaches from an applied economist's
perspective. We first introduce the key ML methods drawing connections to econometric …

The growing application of data-driven analytics in materials science has led to the rise of
materials informatics. Within the arena of data analytics, deep learning has emerged as a …

Global climate change and its impact on human life has become one of our era's greatest
challenges. Despite the urgency, data science has had little impact on furthering our …

Written by a team of international experts, Extremes and Recurrence in Dynamical Systems
presents a unique point of view on the mathematical theory of extremes and on its …