Replica exchange Hybrid Monte Carlo simulations of the ammonia dodecamer and hexadecamer

Venditto, J.G.; Wolf, Sarah; Curotto, E.; Mella, Mariella

doi:10.1016/j.cplett.2015.06.044

Cited by 5 publications

(3 citation statements)

References 45 publications

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“…In 2009, Cheung and Beck 30 applied the hybrid Monte Carlo (HMC) algorithm to update a dynamic linear structure with 31 uncertain parameters; the results show that the HMC algorithm has demonstrated promising results in solving complex higher-dimensional problems. [31][32][33] Boulkaibet et al 34,35 advanced two modified versions of the HMC algorithm-the shadow hybrid Monte Carlo and separable shadow hybrid Monte Carlo algorithms-and applied these for Bayesian finite element model updating; both of these modified versions produced samples gave more accurate results than the HMC algorithm. Lam et al 36 proposed an enhanced MCMC algorithm for solving the structural model updating problem following the Bayesian statistical system identification framework; a novel stopping criterion is developed, and the practical value of the proposed method is demonstrated by its application in a coupled-slab system.…”

Section: Introductionmentioning

confidence: 99%

“…However, the Laplace asymptotic approximation method is extremely computationally expensive and has a very complicated calculation process, and hence it is difficult to use in engineering applications. Therefore, many researchers 18,20–40 have proposed the Markov chain Monte Carlo (MCMC) method to randomly sample the probability distribution, such that the sample distribution converges to the target probability distribution. To address the problems of traditional MCMC algorithms (i.e., slow convergence and applicability to only low‐dimensional situations), Beck and Au 24,25 successively proposed a modified Metropolis–Hastings (MMH) algorithm in 2001 and an annealing Metropolis–Hastings (AMH) algorithm in 2002 and performed damage identification of a two‐degree‐of‐freedom moment‐resisting frame using the AMH algorithm.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian estimation approach based on modified SCAM algorithm and its application in structural damage identification

Zhao

Gong

Zuo

et al. 2020

Struct Control Health Monit

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The single-component adaptive Metropolis (SCAM) algorithm, as one of the Markov chain Monte Carlo (MCMC) sampling methods, is very effective at solving high-dimensional posterior joint probability distributions. However, studies show that the SCAM algorithm is prone to generating duplicate samples, thus lowering the sampling efficiency and causing large calculation errors. To solve this problem, a modified SCAM algorithm is proposed in the present study. In the modified algorithm, the expression of the variance of the proposal distribution is redefined so that the Markov chain formed by the sample sequence is relatively stable. With the obtained structural response, the posterior joint probability distribution of the physical parameters of the structure is achieved using Bayesian theory, which is then combined with the modified SCAM algorithm to solve the posterior marginal probability distribution and the optimal estimates. Hence, the structural damage is identified. The validity, reliability, and practicability of the modified SCAM algorithm are verified through theoretical analysis, a numerical simulation of the structural example, and a shaking table test. The results show that the modified SCAM algorithm not only enhances the accuracy of the calculation results but also increases the sampling efficiency. This method can be used as a reference for physical parameter identification and damage assessment of the engineering structures.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian estimation approach based on modified SCAM algorithm and its application in structural damage identification

Zhao

Gong

Zuo

et al. 2020

Struct Control Health Monit

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Adaptive Markov Chain Monte Carlo Method for Finite Element Model Updating

2016

Probabilistic Finite Element Model Updating Using Bayesian Statistics

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Ion-Stockmayer clusters: Minima, classical thermodynamics, and variational ground state estimates of Li+(CH3NO2)n (n = 1–20)

Curotto

2015

The Journal of Chemical Physics

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Structural optimizations, classical NVT ensemble, and variational Monte Carlo simulations of ion Stockmayer clusters parameterized to approximate the Li(+)(CH3NO2)n (n = 1-20) systems are performed. The Metropolis algorithm enhanced by the parallel tempering strategy is used to measure internal energies and heat capacities, and a parallel version of the genetic algorithm is employed to obtain the most important minima. The first solvation sheath is octahedral and this feature remains the dominant theme in the structure of clusters with n ≥ 6. The first "magic number" is identified using the adiabatic solvent dissociation energy, and it marks the completion of the second solvation layer for the lithium ion-nitromethane clusters. It corresponds to the n = 18 system, a solvated ion with the first sheath having octahedral symmetry, weakly bound to an eight-membered and a four-membered ring crowning a vertex of the octahedron. Variational Monte Carlo estimates of the adiabatic solvent dissociation energy reveal that quantum effects further enhance the stability of the n = 18 system relative to its neighbors.

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Replica exchange Hybrid Monte Carlo simulations of the ammonia dodecamer and hexadecamer

Cited by 5 publications

References 45 publications

Bayesian estimation approach based on modified SCAM algorithm and its application in structural damage identification

Bayesian estimation approach based on modified SCAM algorithm and its application in structural damage identification

Adaptive Markov Chain Monte Carlo Method for Finite Element Model Updating

Ion-Stockmayer clusters: Minima, classical thermodynamics, and variational ground state estimates of Li+(CH3NO2)n (n = 1–20)

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