TY - GEN
T1 - Finite element model updating using the separable shadow hybrid Monte Carlo technique
AU - Boulkaibet, I.
AU - Mthembu, L.
AU - Marwala, T.
AU - Friswell, M. I.
AU - Adhikari, S.
N1 - Publisher Copyright:
© The Society for Experimental Mechanics, Inc. 2014.
PY - 2014
Y1 - 2014
N2 - The use of Bayesian techniques in Finite ElementModel (FEM) updating has recently increased. These techniques have the ability to quantify and characterize the uncertainties of dynamic structures. In order to update a FEM, the Bayesian formulation requires the evaluation of the posterior distribution function. For large systems, this functions is either difficult (or not available) to solve in an analytical way. In such cases using sampling techniques can provide good approximations of the Bayesian posterior distribution function. The Hybrid Monte Carlo (HMC) method is a powerful sampling method for solving higher-dimensional complex problems. The HMC uses the molecular dynamics (MD) as a globalMonte Carlo (MC) move to reach areas of high probability. However, the acceptance rate of HMC is sensitive to the system size as well as the time step used to evaluateMD trajectory. To overcome this, we propose the use of the Separable Shadow Hybrid Monte Carlo (S2HMC) method. This method generates samples from a separable shadow Hamiltonian. The accuracy and the efficiency of this sampling method is tested on the updating of a GARTEUR SM-AG19 structure.
AB - The use of Bayesian techniques in Finite ElementModel (FEM) updating has recently increased. These techniques have the ability to quantify and characterize the uncertainties of dynamic structures. In order to update a FEM, the Bayesian formulation requires the evaluation of the posterior distribution function. For large systems, this functions is either difficult (or not available) to solve in an analytical way. In such cases using sampling techniques can provide good approximations of the Bayesian posterior distribution function. The Hybrid Monte Carlo (HMC) method is a powerful sampling method for solving higher-dimensional complex problems. The HMC uses the molecular dynamics (MD) as a globalMonte Carlo (MC) move to reach areas of high probability. However, the acceptance rate of HMC is sensitive to the system size as well as the time step used to evaluateMD trajectory. To overcome this, we propose the use of the Separable Shadow Hybrid Monte Carlo (S2HMC) method. This method generates samples from a separable shadow Hamiltonian. The accuracy and the efficiency of this sampling method is tested on the updating of a GARTEUR SM-AG19 structure.
KW - Bayesian
KW - Finite element model updating
KW - Hybrid Monte Carlo method
KW - Markov chain Monte Carlo
KW - Sampling
KW - Shadow hybrid Monte Carlo
UR - http://www.scopus.com/inward/record.url?scp=84988698653&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-04774-4_26
DO - 10.1007/978-3-319-04774-4_26
M3 - Conference contribution
AN - SCOPUS:84988698653
SN - 9783319008752
SN - 9783319047737
T3 - Conference Proceedings of the Society for Experimental Mechanics Series
SP - 267
EP - 275
BT - Residual Stress, Thermomechanics and Infrared Imaging, Hybrid Techniques and Inverse Problems, - Proceedings of the 2013 Annual Conference on Experimental and Applied Mechanics
A2 - Allemang, Randall
PB - Springer New York LLC
T2 - 32nd IMAC Conference and Exposition on Structural Dynamics, 2014
Y2 - 3 February 2014 through 6 February 2014
ER -