Modeling of dispersion of air pollutants in the atmosphere is one of the most important and
challenging scientific problems. There are several natural and anthropogenic events where …

In this chapter the state-of-the-art in data assimilation for high-dimensional highly nonlinear
systems is reviewed, and recent developments are highlighted. This knowledge is available …

Graph neural networks are increasingly becoming the go-to approach in various fields such
as computer vision, computational biology and chemistry, where data are naturally …

Partial differential equations (PDEs) are indispensable for modeling many physical
phenomena and also commonly used for solving image processing tasks. In the latter area …

Deep neural networks have become invaluable tools for supervised machine learning, eg
classification of text or images. While often offering superior results over traditional …

Recently, deep residual networks have been successfully applied in many computer vision
and natural language processing tasks, pushing the state-of-the-art performance with …

In this book the authors describe the principles and methods behind probabilistic forecasting
and Bayesian data assimilation. Instead of focusing on particular application areas, the …

A normalizing flow is an invertible mapping between an arbitrary probability distribution and
a standard normal distribution; it can be used for density estimation and statistical inference …

This book presents a broad overview of numerical methods for students and professionals in
computationally oriented disciplines who need to solve mathematical problems. It differs …

Invertible neural networks (INNs) have been used to design generative models, implement
memory-saving gradient computation, and solve inverse problems. In this work, we show …