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File dakota.changes of Package dakota
------------------------------------------------------------------- Sun Jan 1 15:50:18 UTC 2023 - Stefan BrĂ¼ns <stefan.bruens@rwth-aachen.de> - Update to 6.17 * Too many changes to list, for details see https://dakota.sandia.gov/content/dakota-617 - Move (unversioned) libraries from devel subpackage to main package. - Clean up spec file, remove some packaging issues ------------------------------------------------------------------- Tue Aug 11 21:28:31 UTC 2020 - Chris Coutinho <chrisbcoutinho@gmail.com> - Update to 6.12 * The efficient_global method for optimization and least squares now supports concurrent refinement (adding multiple points). * (Experimental) The MIT Uncertainty Quantification (MUQ) MUQ2 library (Parno, Davis, Marzouk, et al.) enhances Dakota's Bayesian inference capability with new Markov Chain Monte Carlo (MCMC) sampling methods. MCMC samplers available in Dakota (under method > bayes_calibration > muq) include Metropolis-Hastings and Adaptive Metropolis. Future work will activate MUQ's more advanced samplers, including surrogate-based and derivative-enhanced sampling, as well as delayed rejection schemes. * (Experimental) Dakota 6.12 extends functional tensor train (FTT) surrogate models from the C3 library (Gorodetsky, University of Michigan) to support building FTT approximations across a sequence of model fidelities (multifidelity FTT) or model resolutions (multilevel FTT). ------------------------------------------------------------------- Tue Jun 4 21:30:24 UTC 2019 - Chris Coutinho <chrisbcoutinho@gmail.com> - Switch from python2 to python3 interface - Add memory-constraints build requirement to handle memory requirements ------------------------------------------------------------------- Fri May 17 21:42:26 UTC 2019 - Chris Coutinho <chrisbcoutinho@gmail.com> - Update to 6.10 * Evaluation data (variables and responses) may now be output to disk in HDF5 format. HDF5 support has been added to all of our downloads. See the Dakota HDF5 Output section of the Reference Manual for full details. * Capabilities for multilevel polynomial chaos expansion (ML PCE) and stochastic collocation (MC SC) have been expanded and hardened to improve their efficiency, completeness, and accuracy. ------------------------------------------------------------------- Mon Mar 11 15:15:54 UTC 2019 - Chris Coutinho <c.coutinho@redstack.nl> - Add a dakota.pth file for python package imports - Remove shebang patches, just use a few oneliners in spec file ------------------------------------------------------------------- Sat Mar 9 13:54:28 UTC 2019 - Chris Coutinho <chrisbcoutinho@gmail.com> - Remove all '%exclude' statements, just remove after installing - Move all .so and .so.* files to devel - clean up distribution conditionals ------------------------------------------------------------------- Tue Mar 5 19:32:25 UTC 2019 - Chris Coutinho <c.coutinho@redstack.nl> - Make an optional dependency on Trilinos package in science repo ------------------------------------------------------------------- Sun Mar 3 17:49:03 UTC 2019 - Chris Coutinho <chrisbcoutinho@gmail.com> - Added four patches 1. install-destination.patch: Dakota has hard-coded its target library destination to 'lib', so this patch changes that to be a cache variable, and also changes all its subpackages to read that 2. env-script.patch: This patch stops rpmlint from complaining about a dependency on 'env' 3. shebang.patch: Adds a shebang to an executable shell script 4. JEGA.patch: Fixes a bug known to upstream that causes a compile error ------------------------------------------------------------------- Thu Feb 28 07:33:33 UTC 2019 - Chris Coutinho <chrisbcoutinho@gmail.com> - Move examples to example package, update doc/license install ------------------------------------------------------------------- Wed Feb 27 00:40:11 UTC 2019 - Chris Coutinho <c.coutinho@redstack.nl> - Initial commit Dakota 6.9
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