Local Approximations for Subset Simulation method --------------------------------------------------------------------------- Created by: Kenan Šehić (kense@dtu.dk; mrsehickenan@gmail.com) Department of Applied Mathematics and Computer Science Technical University of Denmark Licence: Copyright (C) 2019 Kenan Šehić DTU Compute, Technical University of Denmark Cite: Šehić K., Karamehmedović M.: Estimation of Failure Probabilities via Local Subset Approximations, TBD --------------------------------------------------------------------------- Version December 2019 --------------------------------------------------------------------------- * Main Ref: Subset Simulation Method - Felipe Uribe (felipe.uribe@tum.de) https://www.bgu.tum.de/era/software/software00/subset-simulation/ Papaioannou I., Betz W., Zwirglmaier K., Straub D.: MCMC algorithms for subset simulation. Probabilistic Engineering Mechanics, 41: 89-103. P. R. Conrad, Y. M. Marzouk,N. S. Pillai and A. Smith: Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations. Journal of the American Statistical Association, Volume 111, 2016 - Issue 516, Pages 1591-1607 --------------------------------------------------------------------------- 1. You will need to have MATLAB2019B or similar. 2. Download folders to your directory. 3. Select which Gaussian -gp or polynomial regression -poly folder. 4. Open SuS_xxx.m 5. Follow comments 6. Run and explore If you find any mistake or bug or you have comments, please contact me at kense@dtu.dk or mrsehickenan@gmail.com. Thank you!
ksehic/Local-Approximations-for-SuS
Local Approximations for Subset Simulation method. It is a sequential design (active learning) based on polynomial regression and Gaussian process to quantify extreme events efficiently.
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