Supercomputing and Computation



ORNL's research focuses on the development of several transformational methodologies related to the efficient, accurate and robust computation of statistical quantities of interest, i.e., the information used by engineers and decision makers, that are determined from solutions of complex stochastic simulations. Our objective is to harness powerful HPC architectures through innovative approaches for UQ that provide quantitative bounds on the applicability of extreme scale calculations. We are currently concentrating on the development of rigorous mathematical procedures for combating the curse of dimensionality and for exploiting multicore extreme parallelism. These methods are based on multi-dimensional multi-resolution adaptive sparse stochastic non-intrusive and semi-intruisve approximations. The latter paradigm builds on existing progress in generic programming to selectively couple ensembles of our hierarchical decomposition while enabling advanced recycling and block solver techniques. Finally, our massively scalable UQ framework is being made available through an ORNL Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN (TASMANIAN).


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