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@ARTICLE{Cheng:904446,
      author       = {Cheng, Kai and Lu, Zhenzhou and Xiao, Sinan and Zhang,
                      Xiaobo and Oladyshkin, Sergey and Nowak, Wolfgang},
      title        = {{R}esampling method for reliability-based design
                      optimization based on thermodynamic integration and parallel
                      tempering},
      journal      = {Mechanical systems and signal processing},
      volume       = {156},
      issn         = {0888-3270},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2021-06016},
      pages        = {107630 -},
      year         = {2021},
      note         = {Ein Postprint steht leider nicht zur Verfügung},
      abstract     = {In this paper, a fully decoupled simulation method is
                      proposed for reliability-based design optimization (RBDO)
                      based on thermodynamic integration and parallel tempering
                      (TIPT). We show that the failure probability function and
                      its gradient can be obtained simultaneously with once
                      generalized reliability analysis, and thus the RBDO problem
                      is converted to the traditional optimization problem
                      efficiently. Firstly, the design parameters are deemed as
                      uniformly distributed random variables, and an auxiliary
                      probability density function (PDF) of random design
                      variables is constructed to cover its whole parameter space.
                      Then, based on thermodynamic integration, the estimation of
                      failure probability is converted to a series of simple
                      integration problems with smooth integrand, and they are
                      estimated by running multiple Markov chains using the
                      so-called parallel tempering method. Finally, importance
                      sampling (IS) is used to estimate the failure probability
                      function and its gradient, and the IS samples are obtained
                      by resampling from the existing Markov chains without extra
                      computation. The proposed method is tested with severa
                      benchmarks, and the results show that it provides robust
                      solution for problems with various nonlinear constraints
                      compared to other popular methods, include double-loop Monte
                      Carlo simulation (MCS), Quantile MCS, sequential
                      optimization and reliability assessment, performance measure
                      approach and reliability index approach.},
      cin          = {IBG-3},
      ddc          = {004},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000634833100006},
      doi          = {10.1016/j.ymssp.2021.107630},
      url          = {https://juser.fz-juelich.de/record/904446},
}