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Description:The Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN is a collection of robust libraries for high dimensional integration and interpolation as well as parameter calibration. The code consists of several modules that can be used individually or conjointly. The project is sponsored by Oak Ridge National Laboratory Directed Research and Development as well as the Department of Energy Office for Advanced Scientific Computing Research.
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Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN ORNL Laboratory Directed Research and Development DoE: Office for Advanced Scientific Computing Research Exascale Computing Project Home About Development Team Downloads Manual Contact Us Development Team Miroslav Stoyanov Lead Developer Department of Applied Mathematics Oak Ridge National Laboratory Guannan Zhang Developer Department of Applied Mathematics Oak Ridge National Laboratory John Burkardt Developer Department of Scientific Computing Florida State University Damien T. Lebrun-Grandie Developer Computational Engineering and Energy Sciences Oak Ridge National Laboratory Zack Morrow Developer Developer of the Fourier Sparse Grids North Carolina State University Viktor Reshniak Developer Department of Applied Mathematics Oak Ridge National Laboratory Clayton Webster Principal Investigator Department of Applied Mathematics Oak Ridge National Laboratory ABOUT The Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN is a collection of robust libraries for high dimensional integration and interpolation as well as parameter calibration. The code consists of several modules that can be used individually or conjointly. The project is sponsored by Oak Ridge National Laboratory Directed Research and Development as well as the Department of Energy Office for Advanced Scientific Computing Research. GitHub Repository Dream DiffeRential Evolution Adaptive Metropolis (DREAM) is an algorithm for sampling from a general probability density when only the probability density function is known. The method can be applied to problems of Bayesian inference and optimization, including custom defined models as well as surrogate models constructed with the Sparse Grids module. Refer to the manual for more details. Sparse Grids Sparse Grids is a family of algorithms for constructing multidimensional quadrature and interpolation rules from tensor products of one dimensional such rules. Sparse Grids Module implements a wide variety of one dimensional rules based on global and local function basis. Refer to the Manual for a complete list of the capabilities. Contact For more information about , please contact: Miroslav Stoyanov Email: stoyanovmk@ornl.gov GitHub: https://github.com/ORNL/ Dream Sparse Grids Contact Industry Partners Caterpillar Inc. Ford Motor Company General Motors Partners: ORNL - Directorate - CSM - NCCS - ORNL Disclaimer URL: tasmanian.ornl.gov /index.html Updated: Wednesday, 10-Oct-2018 12:55:49 EDT webmaster...
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