A Modular Subset Simulation Toolbox for Matlab

View Video Presentation: https://doi.org/10.2514/6.2022-1893.vid We present an open, modular toolbox for Matlab that implements an algorithm for Subset Simulation (SuS) with Markov-chain Monte Carlo (MCMC) sampling to estimate occurrence probabilities of rare events, available at http://go.tum.de/605579. The toolbox is built around an object-oriented API that makes understanding the setup easy for the user. Core features include four different MCMC sampling methods, three different limit-state evaluation modes, accelerating slow limit-state function evaluations through surrogate models and through compilation of Simulink models, reusing result data to study parameter sensitivities, sampling dependent parameters from Vine Copula structures, and an instrumentation approach to tracking algorithm progress. We demonstrate in short examples how we use these features to easily analyse the rare-event failure probability and parameter sensitivities of benchmark problems, and for a performance requirement on a closed-loop aircraft simulation model.