Reduced dimensional Gaussian process emulators of parametrized partial differential equations based on Isomap
In this paper, Isomap and kernel Isomap are used to dramatically reduce the dimensionality
of the output space to efficiently construct a Gaussian process emulator of parametrized …
of the output space to efficiently construct a Gaussian process emulator of parametrized …
Deep coregionalization for the emulation of simulation-based spatial-temporal fields
Data-driven surrogate models are widely used for applications such as design optimization
and uncertainty quantification, where repeated evaluations of an expensive simulator are …
and uncertainty quantification, where repeated evaluations of an expensive simulator are …
Anchored analysis of variance Petrov–Galerkin projection schemes for linear stochastic structural dynamics
In this paper, we propose anchored functional analysis of variance Petrov–Galerkin (AAPG)
projection schemes, originally developed in the context of parabolic stochastic partial …
projection schemes, originally developed in the context of parabolic stochastic partial …
Deep coregionalization for the emulation of spatial-temporal fields
Data-driven surrogate models are widely used for applications such as design optimization
and uncertainty quantification, where repeated evaluations of an expensive simulator are …
and uncertainty quantification, where repeated evaluations of an expensive simulator are …
Anchored ANOVA Petrov–Galerkin projection schemes for parabolic stochastic partial differential equations
We present a numerical scheme based on the combination of Hoeffding's functional analysis
of variance (ANOVA) decomposition with stochastic Galerkin projection for solving a class of …
of variance (ANOVA) decomposition with stochastic Galerkin projection for solving a class of …
Some a priori error estimates for finite element approximations of elliptic and parabolic linear stochastic partial differential equations
We study some theoretical aspects of Legendre polynomial chaos based finite element
approximations of elliptic and parabolic linear stochastic partial differential equations …
approximations of elliptic and parabolic linear stochastic partial differential equations …
Space and chaos‐expansion Galerkin proper orthogonal decomposition low‐order discretization of partial differential equations for uncertainty quantification
The quantification of multivariate uncertainties in partial differential equations can easily
exceed any computing capacity unless proper measures are taken to reduce the complexity …
exceed any computing capacity unless proper measures are taken to reduce the complexity …
Space and Chaos-Expansion Galerkin POD Low-order Discretization of PDEs for Uncertainty Quantification
The quantification of multivariate uncertainties in partial differential equations can easily
exceed any computing capacity unless proper measures are taken to reduce the complexity …
exceed any computing capacity unless proper measures are taken to reduce the complexity …
A priori error estimates for finite element approximations of parabolic stochastic partial differential equations with generalized random variables
We consider finite element approximations of parabolic stochastic partial differential
equations (SPDEs) in conjunction with the-weighted temporal discretization scheme. We …
equations (SPDEs) in conjunction with the-weighted temporal discretization scheme. We …
[图书][B] Projection schemes for high-dimensional problems in stochastic structural dynamics
L Gao - 2018 - search.proquest.com
The focus of the present thesis is to formulate efficient schemes to solve high-dimensional
stochastic ordinary differential equations (SODEs) encountered in stochastic structural …
stochastic ordinary differential equations (SODEs) encountered in stochastic structural …