Multifidelity importance sampling
Estimating statistics of model outputs with the Monte Carlo method often requires a large
number of model evaluations. This leads to long runtimes if the model is expensive to …
number of model evaluations. This leads to long runtimes if the model is expensive to …
Spectral tensor-train decomposition
The accurate approximation of high-dimensional functions is an essential task in uncertainty
quantification and many other fields. We propose a new function approximation scheme …
quantification and many other fields. We propose a new function approximation scheme …
Sobol tensor trains for global sensitivity analysis
R Ballester-Ripoll, EG Paredes, R Pajarola - Reliability Engineering & …, 2019 - Elsevier
Sobol indices are a widespread quantitative measure for variance-based global sensitivity
analysis, but computing and utilizing them remains challenging for high-dimensional …
analysis, but computing and utilizing them remains challenging for high-dimensional …
Calibration experimental design considering field response and model uncertainty
Z Hu, D Ao, S Mahadevan - Computer Methods in Applied Mechanics and …, 2017 - Elsevier
Calibration experiment design optimization (CEDO) seeks to identify the optimal values of
experimental inputs in order to maximize the obtained information within testing budget …
experimental inputs in order to maximize the obtained information within testing budget …
Quantification of airfoil geometry-induced aerodynamic uncertainties---comparison of approaches
D Liu, A Litvinenko, C Schillings, V Schulz - SIAM/ASA Journal on Uncertainty …, 2017 - SIAM
Uncertainty quantification in aerodynamic simulations calls for efficient numerical methods to
reduce computational cost, especially for uncertainties caused by random geometry …
reduce computational cost, especially for uncertainties caused by random geometry …
Efficient Markov Chain Monte Carlo for combined Subset Simulation and nonlinear finite element analysis
DKE Green - Computer methods in applied mechanics and …, 2017 - Elsevier
Typical probabilistic problems in an engineering context include rare event probability
estimation for physical models where spatial autocorrelation of material property parameters …
estimation for physical models where spatial autocorrelation of material property parameters …
Entropy-based adaptive design for contour finding and estimating reliability
DA Cole, RB Gramacy, JE Warner… - Journal of Quality …, 2023 - Taylor & Francis
In reliability analysis, methods used to estimate failure probability are often limited by the
costs associated with model evaluations. Many of these methods, such as multifidelity …
costs associated with model evaluations. Many of these methods, such as multifidelity …
A surrogate modeling approach for reliability analysis of a multidisciplinary system with spatio-temporal output
Z Hu, S Mahadevan - Structural and Multidisciplinary Optimization, 2017 - Springer
Reliability analysis of a multidisciplinary system is computationally intensive due to the
involvement of multiple disciplinary models and coupling between the individual models …
involvement of multiple disciplinary models and coupling between the individual models …
Computation of electromagnetic fields scattered from objects with uncertain shapes using multilevel Monte Carlo method
Computational tools for characterizing electromagnetic scattering from objects with uncertain
shapes are needed in various applications ranging from remote sensing at microwave …
shapes are needed in various applications ranging from remote sensing at microwave …
Computation of the response surface in the tensor train data format
We apply the Tensor Train (TT) approximation to construct the Polynomial Chaos Expansion
(PCE) of a random field, and solve the stochastic elliptic diffusion PDE with the stochastic …
(PCE) of a random field, and solve the stochastic elliptic diffusion PDE with the stochastic …