A computational inverse method for identification of non-Gaussian random fields using the Bayesian approach in very high dimension

From MaRDI portal
Publication:660306

DOI10.1016/j.cma.2011.07.005zbMath1230.74241OpenAlexW2065620939MaRDI QIDQ660306

Christian Soize

Publication date: 1 February 2012

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://hal-upec-upem.archives-ouvertes.fr/hal-00684294/file/publi-2011-CMAME-200_45-46_3083-3099-soize-preprint.pdf



Related Items

Spatially-dependent material uncertainties in anisotropic nonlinear elasticity: stochastic modeling, identification, and propagation, Probabilistic learning inference of boundary value problem with uncertainties based on Kullback-Leibler divergence under implicit constraints, Bayesian uncertainty quantification of turbulence models based on high-order adjoint, Bayesian identification and model comparison for random property fields derived from material microstructure, Adaptive method for indirect identification of the statistical properties of random fields in a Bayesian framework, Tensor- and spinor-valued random fields with applications to continuum physics and cosmology, Updating an uncertain and expensive computational model in structural dynamics based on one single target FRF using a probabilistic learning tool, Inverse elastic scattering by random periodic structures, Probabilistic learning constrained by realizations using a weak formulation of Fourier transform of probability measures, An interval framework for uncertain frequency response of multi-cracked beams with application to vibration reduction via tuned mass dampers, Polynomial-chaos-based conditional statistics for probabilistic learning with heterogeneous data applied to atomic collisions of helium on graphite substrate, Unnamed Item, Convergence acceleration of polynomial chaos solutions via sequence transformation, Uncertainty quantification in computational stochastic multiscale analysis of nonlinear elastic materials, Bayesian adaptation of chaos representations using variational inference and sampling on geodesics, Compressed Principal Component Analysis of Non-Gaussian Vectors, A robust solution of a statistical inverse problem in multiscale computational mechanics using an artificial neural network, Reduced model of macro-scale stochastic plasticity identification by Bayesian inference: application to quasi-brittle failure of concrete, A patching algorithm for conditional random fields in modeling material properties, Random field representations for stochastic elliptic boundary value problems and statistical inverse problems



Cites Work