Klaus Hasselmann's revolutionary intuition in climate science was to use the stochasticity
associated with fast weather processes to probe the slow dynamics of the climate system …

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

Data assimilation is concerned with sequentially estimating a temporally evolving state. This
task, which arises in a wide range of scientific and engineering applications, is particularly …

Due to the rapidly changing climate, the frequency and severity of extreme weather is
expected to increase over the coming decades. As fully‐resolved climate simulations remain …

We propose a new data-driven reduced order model (ROM) framework that centers around
the hierarchical structure of the variational multiscale (VMS) methodology and utilizes data …

We present a supervised learning method to learn the propagator map of a dynamical
system from partial and noisy observations. In our computationally cheap and easy-to …

Recent studies have demonstrated that it is possible to combine machine learning with data
assimilation to reconstruct the dynamics of a physical model partially and imperfectly …

Physics-based and first-principles models pervade the engineering and physical sciences,
allowing for the ability to model the dynamics of complex systems with a prescribed …

A common problem in time-series analysis is to predict dynamics with only scalar or partial
observations of the underlying dynamical system. For data on a smooth compact manifold …

We delve into the intriguing realm of reservoir computing to predict the intricate dynamics of
a stochastic FitzHugh–Nagumo neuron model subjected to external noise. Through …