Noisy dynamical models are employed to describe a wide range of phenomena. Since exact
modeling of these phenomena requires access to their microscopic dynamics, whose time …

We describe and implement a computer-assisted approach for accelerating the exploration
of uncharted effective free-energy surfaces (FESs). More generally, the aim is the extraction …

Enforcing sparse structure within learning has led to significant advances in the field of data-
driven discovery of dynamical systems. However, such methods require access not only to …

The atmospheric boundary layer is particularly challenging to model in conditions of stable
stratification, which can be associated with intermittent or unsteady turbulence. We develop …

Complex dynamical systems are notoriously difficult to model because some degrees of
freedom (eg, small scales) may be computationally unresolvable or are incompletely …

Although the governing equations of many systems, when derived from first principles, may
be viewed as known, it is often too expensive to numerically simulate all the interactions they …

Understanding the impact of model error on data assimilation is an important practical topic.
Model error in the subgrid scale is commonly seen in various applications as a natural …

Machine learning is an attractive paradigm to circumvent difficulties associated with the
development and optimization of force-field parameters. In this study, a deep neural network …

The application of stochastic differential equations (SDEs) to the analysis of temporal data
has attracted increasing attention, due to their ability to describe complex dynamics with …

We study the problem of drift estimation for two-scale continuous time series. We set
ourselves in the framework of overdamped Langevin equations, for which a single-scale …