Supercomputing and Computation

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Uncertainty Quantification


A truly predictive science for many problems of scientific and national interest requires advanced mathematical and computational tools that explain how the uncertainties, ubiquitous in all modeling efforts, affect our predictions and understanding of complex phenomena. This remains a fundamental difficulty in most applications that dominate the focus of the DOE mission. Examples include enhancement of reliability of smart energy grids, development of renewable energy technologies, vulnerability analysis of water and power supplies, understanding complex biological networks, climate change estimation and design and licensing of current and future nuclear energy reactors. Often these systems describe physical and biological processes exhibiting highly nonlinear, or even worse, discontinuous/bifurcating phenomena at a diverse set of length and/or time scales. Moreover, predictive simulation of these systems requires significantly more computational effort than high-fidelity deterministic simulations; particularly when the random input data (coefficients, forcing terms, initial and boundary conditions, geometry, etc.) are affected by large amounts of uncertainty. Even for these high-dimensional stochastic problems, simulation code verification, model validation, and uncertainty quantification (UQ) are indispensable tasks demanded to justify a predictive capability in a mathematically and scientifically rigorous manner.

Related Projects

1-3 of 3 Results
 

Multiresolution and Adaptive Numerical Environment for Scientific Simulations (MADNESS)
— Numerical modeling softwares and solvers are key components of successful and accurate simulations for effective predictions and analysis. The results help us to improve our scientific theories as well as product and engineering designs. A representative of the most successful of the traditional software and solver environment is MATLAB with a core that is based on matrix and vector operations.

TASMANIAN
— ORNL's research focuses on the development of several transformational methodologies related to the efficient, accurate and robust computation of statistical quantities of interest.

Scalable stencil-based solver algorithms
— This research concentrates on developing highly scalable algorithms and modular software in support of numerical methods for solving partial differential equations with stencil-based discretization approaches. Stencil codes require only nearest neighbor information in order to perform a numerical update and are thereby amenable to efficient and hybrid parallelization.

 
 
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