Computing Transitions in Macromolecular Systems: Dynamic Importance Sampling

John von Neumann Institute for Computing Phase Transitions in Macromolecular Systems

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Monte-Carlo acceleration: importance sampling and hybrid dynamic systems

The reliability of a complex industrial system can rarely be assessed analytically. As system failure is often a rare event, crude Monte-Carlo methods are prohibitively expensive from a computational point of view. In order to reduce computation times, variance reduction methods such as importance sampling can be used. We propose an adaptation of this method for a class of multi-component dynam...

Predicting slow structural transitions in macromolecular systems: Conformational flooding.

Dynamic Importance Sampling for Queueing Networks

Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even ...

Sampling in Factored Dynamic Systems