With the rapid increase of available data for complex systems, there is great interest in the
extraction of physically relevant information from massive datasets. Recently, a framework …

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 …

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 …

In many physical, technological, social, and economic applications, one is commonly faced
with the task of estimating statistical properties, such as mean first passage times of a …

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 …

In this paper we discuss information-theoretic tools for obtaining optimized coarse-grained
molecular models for both equilibrium and non-equilibrium molecular simulations. The latter …

We consider the problem of estimating unknown parameters in stochastic differential
equations driven by colored noise, which we model as a sequence of Gaussian stationary …

Motivated by the challenge of incorporating data into misspecified and multiscale dynamical
models, we study a McKean–Vlasov equation that contains the data stream as a common …