In spite of continued growth in computational resources the idea of a computational laboratory serving as surrogate to physical reality still faces conceptual and technical challenges. Chief among these is the characterization of physical reality itself, under conditions of incomplete knowledge and information, reflected in observed variability and fluctuations. The quantification of this uncertainty has, in recent years, grown from a collection of scientific ideas into a sub-discipline of computational science that attempts to provide a quantitative description of incomplete knowledge for use in conjunction with model-based computational resources, algorithms, and software.
Uncertainty exists in all branches of science and engineering. Accordingly, in recent reports and initiatives on scientific computing, uncertainty quantification (UQ) has been recognized as a critical element necessary for continued advancement in prediction science, life-cycle design, and societal sustainability. The topic of uncertainty, in general, remains rather nebulous and susceptible to philosophical arguments. Significant progress has been made in recent years within a subset of related problems in science and engineering, namely those for which the behavior can be suitably modeled with conservation or variational laws containing stochastic coefficients. Also in recent years, there has been an increasing awareness of complexity as an essential theoretical challenge in many problems of great societal relevance the hallmarks of which are interacting phenomena, nonlinearities and emergent behavior. Network Science has been developing in response to these challenges and has gained both in mathematical maturity and scope of applicability. In turn, developments in network science have spurred significant research activity in computational social sciences and in particular social networks.
This Workshop on Opportunities and Challenges in Uncertainty Quantification for Com
|Contact: Eric Mankin|
University of Southern California