The principal topic of the workshop will be algorithm development and mathematical analysis of computational probability in the context of complex systems. The aim is for a focused workshop, and we do not plan to cover all approaches to uncertainty quantification. The main methodology will be Monte Carlo methods, and themes will be sensitivity analysis with respect to modeling error, and the role of rare and extreme events. Topics include the analysis of Monte Carlo algorithms using tools from information theory, various methods and approaches to sensitivity analysis, and coarse graining and related methods for model approximation and simplification. Applications include, but are not limited to, problems in materials science, stochastic networks, finance, and risk management.
The workshop is supported by the U.S. Department of Energy Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics program under Award “Mathematical Foundations for Uncertainty Quantification in Materials Design” Number DE-SC-0010539 and the Institute for Pure and Applied Mathematics.
This workshop will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.
Peter Glynn (Stanford University)
Markos Katsoulakis (University of Massachusetts Amherst, Mathematics & Statistics)
Petr Plechac (University of Delaware)