Stochastic field representation using bi-fidelity combination of proper orthogonal decomposition and kriging

DOI10.1016/j.cma.2019.112589zbMath1442.65114OpenAlexW2967927607WikidataQ127334360 ScholiaQ127334360MaRDI QIDQ2179204

Mehrdad Raisee, Arash Mohammadi

Publication date: 12 May 2020

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

Full work available at URL: https://doi.org/10.1016/j.cma.2019.112589

zbMATH Keywords

krigingproper orthogonal decompositionuncertainty quantificationreduced order modelspolynomial chaos expansionbi-fidelity

Mathematics Subject Classification ID

Numerical methods for mathematical programming, optimization and variational techniques (65K99)

Related Items (1)

Efficient uncertainty quantification of CFD problems by combination of proper orthogonal decomposition and compressed sensing

Cites Work

• Unnamed Item
• A partitioned model order reduction approach to rationalise computational expenses in nonlinear fracture mechanics
• A hybrid anchored-ANOVA - POD/Kriging method for uncertainty quantification in unsteady high-fidelity CFD simulations
• Adaptive sparse polynomial chaos expansion based on least angle regression
• A new surrogate modeling technique combining Kriging and polynomial chaos expansions - application to uncertainty analysis in computational dosimetry
• Accurate solutions to the square thermally driven cavity at high Rayleigh number
• Stochastic model reduction for chaos representations
• Structural optimization using kriging approximation
• Design and analysis of computer experiments. With comments and a rejoinder by the authors
• An efficient non-intrusive reduced basis model for high dimensional stochastic problems in CFD
• A low-rank control variate for multilevel Monte Carlo simulation of high-dimensional uncertain systems
• A domain decomposition non-intrusive reduced order model for turbulent flows
• Stochastic approaches to uncertainty quantification in CFD simulations
• Cross validation and maximum likelihood estimations of hyper-parameters of Gaussian processes with model misspecification
• A stochastic projection method for fluid flow. II: Random process
• Modeling uncertainty in flow simulations via generalized polynomial chaos.
• Data-driven reduced order modeling for time-dependent problems
• Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression
• On the flow field and performance of a centrifugal pump under operational and geometrical uncertainties
• An efficient multifidelity \(\ell_1\)-minimization method for sparse polynomial chaos
• Non-intrusive reduced order modelling of fluid-structure interactions
• Spectral representation of stochastic field data using sparse polynomial chaos expansions
• A domain decomposition strategy for reduced order models. Application to the incompressible Navier-Stokes equations
• A non-intrusive model reduction approach for polynomial chaos expansion using proper orthogonal decomposition
• Optimization using surrogate models and partially converged computational fluid dynamics simulations
• Stochastic finite element: a non intrusive approach by regression
• Algorithm 659
• The Predictive Sample Reuse Method with Applications
• Non‐intrusive reduced‐order modelling of the Navier–Stokes equations based on RBF interpolation
• POLYNOMIAL-CHAOS-BASED KRIGING
• Advanced Lectures on Machine Learning

This page was built for publication: Stochastic field representation using bi-fidelity combination of proper orthogonal decomposition and kriging