Pages that link to "Item:Q4785938"

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

Displaying 50 items.

• On surrogate learning for linear stability assessment of Navier-Stokes equations with stochastic viscosity. (Q2093340) (← links)
• Multifidelity data fusion in convolutional encoder/decoder networks (Q2099723) (← links)
• GenMod: a generative modeling approach for spectral representation of PDEs with random inputs (Q2099749) (← links)
• A multi-element non-intrusive polynomial chaos method using agglomerative clustering based on the derivatives to study irregular and discontinuous quantities of interest (Q2106992) (← links)
• Quantifying multiple uncertainties in modelling shallow water-sediment flows: a stochastic Galerkin framework with Haar wavelet expansion and an operator-splitting approach (Q2109449) (← links)
• Probabilistic CFD analysis on the flow field and performance of the FDA centrifugal blood pump (Q2109726) (← links)
• Multigroup-like MC resolution of generalised polynomial chaos reduced models of the uncertain linear Boltzmann equation (+discussion on hybrid intrusive/non-intrusive uncertainty propagation) (Q2112538) (← links)
• A priori error estimate of perturbation method for optimal control problem governed by elliptic PDEs with small uncertainties (Q2114838) (← links)
• Bi-fidelity reduced polynomial chaos expansion for uncertainty quantification (Q2115584) (← links)
• Feasibility of DEIM for retrieving the initial field via dimensionality reduction (Q2120024) (← links)
• Computing the density function of complex models with randomness by using polynomial expansions and the RVT technique. Application to the SIR epidemic model (Q2120398) (← links)
• Solving inverse problems using conditional invertible neural networks (Q2120777) (← links)
• Analysis of geometric uncertainties in CFD problems solved by RBF-FD meshless method (Q2123743) (← links)
• A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables (Q2124009) (← links)
• Long duration response evaluation of linear structural system with random system properties using time dependent polynomial chaos (Q2124553) (← links)
• Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models (Q2124564) (← links)
• Flow-driven spectral chaos (FSC) method for simulating long-time dynamics of arbitrary-order non-linear stochastic dynamical systems (Q2124875) (← links)
• Optimal design for kernel interpolation: applications to uncertainty quantification (Q2124880) (← links)
• Uncertainty quantification of viscoelastic parameters in arterial hemodynamics with the a-FSI blood flow model (Q2124891) (← links)
• Weighted essentially non-oscillatory stochastic Galerkin approximation for hyperbolic conservation laws (Q2125426) (← links)
• Intrusive acceleration strategies for uncertainty quantification for hyperbolic systems of conservation laws (Q2125463) (← links)
• Estimation of dynamic systems using a method of characteristics filter (Q2125516) (← links)
• An adaptive hp-version stochastic Galerkin method for constrained optimal control problem governed by random reaction diffusion equations (Q2125889) (← links)
• Spectral convergence of probability densities for forward problems in uncertainty quantification (Q2126139) (← links)
• Nonlinear sparse Bayesian learning for physics-based models (Q2126972) (← links)
• Transfer learning based multi-fidelity physics informed deep neural network (Q2127006) (← links)
• Stochastic gradient descent for semilinear elliptic equations with uncertainties (Q2127008) (← links)
• Physically interpretable machine learning algorithm on multidimensional non-linear fields (Q2128352) (← links)
• A stochastic collocation method based on sparse grids for a stochastic Stokes-Darcy model (Q2129156) (← links)
• Bayesian model inversion using stochastic spectral embedding (Q2131057) (← links)
• An efficient hybrid method for uncertainty quantification (Q2132433) (← links)
• Generalized polynomial chaos-informed efficient stochastic kriging (Q2133029) (← links)
• Hyperbolicity-preserving and well-balanced stochastic Galerkin method for two-dimensional shallow water equations (Q2133577) (← links)
• Bayesian inversion using adaptive polynomial chaos kriging within subset simulation (Q2133745) (← links)
• Clustered active-subspace based local Gaussian process emulator for high-dimensional and complex computer models (Q2134712) (← links)
• Simulation of the 3D hyperelastic behavior of ventricular myocardium using a finite-element based neural-network approach (Q2136715) (← links)
• Spatially-dependent material uncertainties in anisotropic nonlinear elasticity: stochastic modeling, identification, and propagation (Q2136730) (← links)
• Normalizing field flows: solving forward and inverse stochastic differential equations using physics-informed flow models (Q2138012) (← links)
• A spectral method for stochastic fractional PDEs using dynamically-orthogonal/bi-orthogonal decomposition (Q2138018) (← links)
• A sample-efficient deep learning method for multivariate uncertainty qualification of acoustic-vibration interaction problems (Q2138808) (← links)
• Neural network training using \(\ell_1\)-regularization and bi-fidelity data (Q2138992) (← links)
• Uncertainty quantification in hierarchical vehicular flow models (Q2140232) (← links)
• Block triangular preconditioning for stochastic Galerkin method (Q2141595) (← links)
• Logarithmic gradient transformation and chaos expansion of Itô processes (Q2141736) (← links)
• Probabilistic learning inference of boundary value problem with uncertainties based on Kullback-Leibler divergence under implicit constraints (Q2142219) (← links)
• Entropy stable Galerkin methods with suitable quadrature rules for hyperbolic systems with random inputs (Q2149052) (← links)
• A stochastic Galerkin method for Maxwell equations with uncertainty (Q2153556) (← links)
• A modular nonlinear stochastic finite element formulation for uncertainty estimation (Q2156767) (← links)
• An asymptotically compatible probabilistic collocation method for randomly heterogeneous nonlocal problems (Q2157079) (← links)
• Stochastic Galerkin methods for the Boltzmann-Poisson system (Q2157121) (← links)