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This paper studies a non-random-walk Markov Chain Monte Carlo method, namelythe Hamiltonian Monte Carlo (HMC) method in the context of Subset Simulationused for structural reliability analysis. The HMC method relies on adeterministic mechanism inspired by Hamiltonian dynamics to propose samplesfollowing a target probability distribution. The method alleviates the randomwalk behavior to achieve a more effective and consistent exploration of theprobability space compared to standard Gibbs or Metropolis-Hastings techniques.After a brief review of the basic concepts of the HMC method and itscomputational details, two algorithms are proposed to facilitate theapplication of the HMC method to Subset Simulation in structural reliabilityanalysis. Next, the behavior of the two HMC algorithms is illustrated usingsimple probability distribution models. Finally, the accuracy and efficiency ofSubset Simulation employing the two HMC algorithms are tested using variousreliability examples. The supporting source code and data are available fordownload at (the URL that will become available once the paper is accepted).

Hamiltonian Monte Carlo Methods for Subset Simulation in Reliability Analysis