Pages that link to "Item:Q4785938"

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

Displaying 50 items.

• An iterative polynomial chaos approach for solution of structural mechanics problem with Gaussian material property (Q2221370) (← links)
• Level set methods for stochastic discontinuity detection in nonlinear problems (Q2221452) (← links)
• Bi-fidelity stochastic gradient descent for structural optimization under uncertainty (Q2221705) (← links)
• Adversarial uncertainty quantification in physics-informed neural networks (Q2222278) (← links)
• Hyperbolic stochastic Galerkin formulation for the \(p\)-system (Q2222336) (← links)
• Data assimilation for models with parametric uncertainty (Q2222452) (← links)
• A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the small data regime (Q2222510) (← links)
• Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems (Q2222519) (← links)
• Gaussian wave packet transform based numerical scheme for the semi-classical Schrödinger equation with random inputs (Q2222699) (← links)
• Bi-fidelity approximation for uncertainty quantification and sensitivity analysis of irradiated particle-laden turbulence (Q2222797) (← links)
• Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (Q2223019) (← links)
• Sensitivity analysis of random linear dynamical systems using quadratic outputs (Q2223804) (← links)
• Non-intrusive framework of reduced-order modeling based on proper orthogonal decomposition and polynomial chaos expansion (Q2226314) (← links)
• Constructing adaptive generalized polynomial chaos method to measure the uncertainty in continuous models: a computational approach (Q2228590) (← links)
• Sensitivity analysis and model order reduction for random linear dynamical systems (Q2228628) (← links)
• Oscillation mitigation of hyperbolicity-preserving intrusive uncertainty quantification methods for systems of conservation laws (Q2229920) (← links)
• Uncertainty damping in kinetic traffic models by driver-assist controls (Q2230375) (← links)
• A parallel dynamic asynchronous framework for uncertainty quantification by hierarchical Monte Carlo algorithms (Q2233963) (← links)
• A non-intrusive stochastic phase field method for crack propagation in functionally graded materials (Q2234547) (← links)
• Uncertainty quantification of stochastic epidemic SIR models using B-spline polynomial chaos (Q2235123) (← links)
• Structural stochastic responses determination via a sample-based stochastic finite element method (Q2236977) (← links)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures (Q2237801) (← links)
• The role of surrogate models in the development of digital twins of dynamic systems (Q2241783) (← links)
• On the numerical simulation of a confined cavitating tip leakage vortex under geometrical and operational uncertainties (Q2245289) (← links)
• Intrusive generalized polynomial chaos with asynchronous time integration for the solution of the unsteady Navier-Stokes equations (Q2245366) (← links)
• On numerical uncertainties in scale-resolving simulations of canonical wall turbulence (Q2245432) (← links)
• Analysis and comparison of turbulence model coefficient uncertainty for canonical flow problems (Q2245436) (← links)
• A high-order stochastic Galerkin code for the compressible Euler and Navier-Stokes equations (Q2245460) (← links)
• Hemodynamics and stresses in numerical simulations of the thoracic aorta: stochastic sensitivity analysis to inlet flow-rate waveform (Q2245543) (← links)
• Option pricing with polynomial chaos expansion stochastic bridge interpolators and signed path dependence (Q2245957) (← links)
• Variance-based adaptive sequential sampling for polynomial chaos expansion (Q2246308) (← links)
• Accelerated basis adaptation in homogeneous chaos spaces (Q2246311) (← links)
• A hybrid sequential sampling strategy for sparse polynomial chaos expansion based on compressive sampling and Bayesian experimental design (Q2246331) (← links)
• Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian processes (Q2246340) (← links)
• A weight initialization based on the linear product structure for neural networks (Q2247166) (← links)
• An adaptive wavelet optimized finite difference B-spline polynomial chaos method for random partial differential equations (Q2247175) (← links)
• Segmentation of stochastic images using level set propagation with uncertain speed (Q2251236) (← links)
• Stochastic collocation and stochastic Galerkin methods for linear differential algebraic equations (Q2252372) (← links)
• Numerical comparison of three stochastic methods for nonlinear PN junction problems (Q2259127) (← links)
• Efficient uncertainty quantification with the polynomial chaos method for stiff systems (Q2271597) (← links)
• Galerkin methods for stochastic hyperbolic problems using bi-orthogonal polynomials (Q2276409) (← links)
• Generalised polynomial chaos for a class of linear conservation laws (Q2276411) (← links)
• A finite element method for Maxwell polynomial chaos Debye model (Q2279232) (← links)
• On the parametric uncertainty quantification of the Rothermel's rate of spread model (Q2284414) (← links)
• Multivariate function approximations using the D-MORPH algorithm (Q2285865) (← links)
• On stochastic linear systems with zonotopic support sets (Q2288639) (← links)
• A new hybrid uncertainty optimization method for structures using orthogonal series expansion (Q2290138) (← links)
• Analytical approximation and numerical studies of one-dimensional elliptic equation with random coefficients (Q2290898) (← links)
• Certified offline-free reduced basis (COFRB) methods for stochastic differential equations driven by arbitrary types of noise (Q2291860) (← links)
• A polynomial chaos expanded hybrid fuzzy-stochastic model for transversely fiber reinforced plastics (Q2293706) (← links)