SIAMUQ16 - Slides Minisymp Software4UQ: Difference between revisions
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* [http://www5.in.tum.de/persons/neckel/siamuq16_slides_minisymp/2016_chaospy_tennoe.pdf Q. Duan: Uncertainty Quantification Python Laboratory (UQ-PyL) – A GUI For Parametric Uncertainty Analysis of Large Complex Dynamical Models] | * [http://www5.in.tum.de/persons/neckel/siamuq16_slides_minisymp/2016_chaospy_tennoe.pdf Q. Duan: Uncertainty Quantification Python Laboratory (UQ-PyL) – A GUI For Parametric Uncertainty Analysis of Large Complex Dynamical Models] | ||
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Revision as of 15:46, 27 April 2016
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At conference on Uncertainty Quantification 2016, Dirk Pflüger and Tobias Neckel organised a minisymposium on Software for UQ.
The presentations of most of the speakers are collected here, in the order of appearance:
- D. Pflüger: Adaptive Sparse Grids for UQ with SG++
- B. Debusschere: UQTk: a C++/Python Toolkit for Uncertainty Quantification
- E. Patelli: Opencossan: A Open Matlab Tool for Dealing with Randomness, Imprecision and Vagueness
- A. Davis: MUQ (MIT Uncertainty Quantification): A Flexible Software Framework for Algorithms and Applications
- D. McDougall: The Parallel C++ Statistical Library Queso: Quantification of Uncertainty for Estimation, Simulation and Optimization
- D. Tennoe: Chaospy: A Modular Implementation of Polynomial Chaos Expansions and Monte Carlo Methods
- P. Hough: Recent Advances in Dakota UQ
- P. Qian: Handling Large-Scale Uncertainty Quantification with SmartUQ
- S. Marelli: Advancements in the Uqlab Framework for Uncertainty Quantification
- C. Tong: PSUADE: A Software Toolkit for Uncertainty Quantification
- Q. Duan: Uncertainty Quantification Python Laboratory (UQ-PyL) – A GUI For Parametric Uncertainty Analysis of Large Complex Dynamical Models
