The objective of molecular dynamics computations is to infer macroscopic properties of
matter from atomistic models via averages with respect to probability measures dictated by …

Solving inverse problems without the use of derivatives or adjoints of the forward model is
highly desirable in many applications arising in science and engineering. In this paper we …

The Zig-Zag process and super-efficient sampling for Bayesian analysis of big data Page 1 The
Annals of Statistics 2019, Vol. 47, No. 3, 1288–1320 https://doi.org/10.1214/18-AOS1715 © …

To improve the efficiency of Monte Carlo estimation, practitioners are turning to biased
Markov chain Monte Carlo procedures that trade off asymptotic exactness for computational …

The Koopman operator has become an essential tool for data-driven approximation of
dynamical (control) systems, eg, via extended dynamic mode decomposition. Despite its …

We derive a variational expression for the correlation time of physical observables in steady-
state diffusive systems. As a consequence of this variational expression, we obtain lower …

Finding diverse and representative Pareto solutions from the Pareto front is a key challenge
in multi-objective optimization (MOO). In this work, we propose a novel gradient-based …

Sampling the Boltzmann distribution using forces that violate detailed balance can be faster
than with the equilibrium evolution, but the acceleration depends on the nature of the …

Stein's method for measuring convergence to a continuous target distribution relies on an
operator characterizing the target and Stein factor bounds on the solutions of an associated …

The zigzag process is a piecewise deterministic Markov process which can be used in
aMCMC framework to sample from a given target distribution. We prove the convergence of …