Pages that link to "Item:Q4785938"

The following pages link to The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (Q4785938):

Displaying 50 items.

• Low-dimensional spatial embedding method for shape uncertainty quantification in acoustic scattering by 2D star shaped obstacles (Q1999878) (← links)
• Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification (Q2002273) (← links)
• Joint chance constrained input shaping (Q2005416) (← links)
• Uncertainty quantification in discrete fracture network models: stochastic fracture transmissivity (Q2006183) (← links)
• Uncertainty quantification for a 1D thermo-hyperelastic coupled problem using polynomial chaos projection and \(p\)-FEMs (Q2006464) (← links)
• Hierarchical preconditioning for the stochastic Galerkin method: upper bounds to the strengthened CBS constants (Q2006646) (← links)
• Explicit cost bounds of stochastic Galerkin approximations for parameterized PDEs with random coefficients (Q2007265) (← links)
• Improving adaptive generalized polynomial chaos method to solve nonlinear random differential equations by the random variable transformation technique (Q2007357) (← links)
• Bayesian model calibration and optimization of surfactant-polymer flooding (Q2009830) (← links)
• An efficient SPDE approach for El Niño (Q2010682) (← links)
• A dynamic bi-orthogonal field equation approach to efficient Bayesian inversion (Q2011888) (← links)
• Dealing with dependent uncertainty in modelling: a comparative study case through the Airy equation (Q2015305) (← links)
• An intrusive hybrid method for discontinuous two-phase flow under uncertainty (Q2016175) (← links)
• Stochastic finite element analysis of layered composite beams with spatially varying non-Gaussian inhomogeneities (Q2016573) (← links)
• Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold (Q2020284) (← links)
• Efficient reliability analysis with a CDA-based dimension-reduction model and polynomial chaos expansion (Q2020745) (← links)
• Modeling strength and failure variability due to porosity in additively manufactured metals (Q2020756) (← links)
• Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network (Q2020800) (← links)
• A two-stage surrogate model for neo-Hookean problems based on adaptive proper orthogonal decomposition and hierarchical tensor approximation (Q2020966) (← links)
• Reduced model of macro-scale stochastic plasticity identification by Bayesian inference: application to quasi-brittle failure of concrete (Q2021054) (← links)
• Hybrid uncertainty analysis of functionally graded plates via multiple-imprecise-random-field modelling of uncertain material properties (Q2021161) (← links)
• Data fusion for uncertainty quantification with non-intrusive polynomial chaos (Q2021255) (← links)
• A matrix-free isogeometric Galerkin method for Karhunen-Loève approximation of random fields using tensor product splines, tensor contraction and interpolation based quadrature (Q2021879) (← links)
• Hybrid topology/shape optimization under uncertainty for actively-cooled nature-inspired microvascular composites (Q2022063) (← links)
• Poly-Sinc solution of stochastic elliptic differential equations (Q2028544) (← links)
• Spectral methods for nonlinear functionals and functional differential equations (Q2028689) (← links)
• Uncertainty quantification for the random viscous Burgers' partial differential equation by using the differential transform method (Q2033035) (← links)
• A generalized multi-fidelity simulation method using sparse polynomial chaos expansion (Q2033075) (← links)
• Generating probability distributions on intervals and spheres with application to finite element method (Q2034905) (← links)
• Flow-driven spectral chaos (FSC) method for long-time integration of second-order stochastic dynamical systems (Q2043180) (← links)
• A splitting/polynomial chaos expansion approach for stochastic evolution equations (Q2044634) (← links)
• Stochastic collocation with hierarchical extended B-splines on sparse grids (Q2050362) (← links)
• On the computation of recurrence coefficients for univariate orthogonal polynomials (Q2051060) (← links)
• Sensitivity analysis of queueing models based on polynomial chaos approach (Q2053746) (← links)
• Stability properties of a projector-splitting scheme for dynamical low rank approximation of random parabolic equations (Q2055995) (← links)
• Derivative-informed projected neural networks for high-dimensional parametric maps governed by PDEs (Q2060092) (← links)
• Adaboost-based ensemble of polynomial chaos expansion with adaptive sampling (Q2060149) (← links)
• Advanced computational technique based on kriging and Polynomial Chaos Expansion for structural stability of mechanical systems with uncertainties (Q2061435) (← links)
• Reliable crack detection in a rotor system with uncertainties via advanced simulation models based on kriging and polynomial chaos expansion (Q2063401) (← links)
• Surrogate-based Bayesian comparison of computationally expensive models: application to microbially induced calcite precipitation (Q2065814) (← links)
• A multigrid multilevel Monte Carlo method for Stokes-Darcy model with random hydraulic conductivity and Beavers-Joseph condition (Q2067300) (← links)
• Fast approximation by periodic kernel-based lattice-point interpolation with application in uncertainty quantification (Q2068357) (← links)
• A multi-fidelity polynomial chaos-greedy Kaczmarz approach for resource-efficient uncertainty quantification on limited budget (Q2072434) (← links)
• A realizable filtered intrusive polynomial moment method (Q2075967) (← links)
• Robust optimization of nonlinear energy sinks used for mitigation of friction-induced limit cycle oscillations (Q2077513) (← links)
• Quantify uncertainty by estimating the probability density function of the output of interest using MLMC based Bayes method (Q2083326) (← links)
• Learning ``best'' kernels from data in Gaussian process regression. With application to aerodynamics (Q2083686) (← links)
• On the use of sparse Bayesian learning-based polynomial chaos expansion for global reliability sensitivity analysis (Q2087515) (← links)
• Propagation of uncertainties in density-driven flow (Q2091293) (← links)
• Efficiently transforming from values of a function on a sparse grid to basis coefficients (Q2091301) (← links)