Extreme weather events have significant consequences, dominating the impact of climate on
society. While high‐resolution weather models can forecast many types of extreme events …

Estimating the likelihood, timing, and nature of events is a major goal of modeling stochastic
dynamical systems. When the event is rare in comparison with the timescales of simulation …

Understanding dynamics in complex systems is challenging because there are many
degrees of freedom, and those that are most important for describing events of interest are …

Weighted ensemble (WE) is an enhanced sampling method based on periodically
replicating and pruning trajectories generated in parallel. WE has grown increasingly …

Blocking events are an important cause of extreme weather, especially long‐lasting blocking
events that trap weather systems in place. The duration of blocking events is, however …

Stochastic dynamics, such as molecular dynamics, are important in many scientific
applications. However, summarizing and analyzing the results of such simulations is often …

Transition path theory (TPT) offers a powerful formalism for extracting the rate and
mechanism of rare dynamical transitions between metastable states. Most applications of …

We quantify the efficiency of temporal difference (TD) learning over the direct, or Monte
Carlo (MC), estimator for policy evaluation in reinforcement learning, with an emphasis on …

A fundamental problem in computer simulation of systems of biophysical interest is the
separation of timescales. This refers to the fact that stable integration of the equations that …

Stochastic dynamics are prevalent throughout many scientific disciplines where finding
useful compact models is an ongoing pursuit. However, the simulations involved are often …