Abstract Machine learning (ML) is increasing in popularity in the field of weather and climate
modelling. Applications range from improved solvers and preconditioners, to …

Reservoir computing is a best-in-class machine learning algorithm for processing
information generated by dynamical systems using observed time-series data. Importantly, it …

The idea of using machine learning (ML) methods to reconstruct the dynamics of a system is
the topic of recent studies in the geosciences, in which the key output is a surrogate model …

Reservoir computing is a machine learning method that is closely linked to dynamical
systems theory. This connection is highlighted in a brief introduction to the general concept …

The paper investigates the applicability of machine learning (ML) to weather prediction by
building a reservoir computing‐based, low‐resolution, global prediction model. The model is …

Machine learning (ML), a subset of artificial intelligence, refers to methods that have the
ability to “learn” from experience, enabling them to carry out designated tasks. Examples of …

The development of data-informed predictive models for dynamical systems is of
widespread interest in many disciplines. We present a unifying framework for blending …

Abstract Machine learning (ML), a computational self-learning platform, is expected to be
applied in a variety of settings. ML, on the other hand, uses a model built with a learning …

This paper describes an implementation of the combined hybrid‐parallel prediction (CHyPP)
approach of Wikner et al.(2020), https://doi. org/10.1063/5.0005541 on a low‐resolution …

In this letter, we consider the problem of deriving and incorporating accurate dynamic
models for model predictive control (MPC) with an application to quadrotor control. MPC …