Journal of Scientific Computing, ISSN 0885-7474, 8/2015, Volume 64, Issue 2, pp. 341 - 367

Meshfree methods based on radial basis function (RBF) approximation are of interest for numerical solution of partial differential equations (PDEs) because...

MSC 65M70 | Computational Mathematics and Numerical Analysis | RBF–PUM | Theoretical, Mathematical and Computational Physics | Convection–diffusion equation | Partition of unity | Mathematics | Meshfree | Radial basis function | MSC 35K15 | American option | Algorithms | Appl.Mathematics/Computational Methods of Engineering | Collocation method | STENCILS | MATHEMATICS, APPLIED | STOCHASTIC VOLATILITY | INTERPOLATION | PRICING AMERICAN OPTIONS | PENALTY METHODS | RBF-PUM | Convection-diffusion equation | Methods | Pricing | Naturvetenskap | Natural Sciences | Beräkningsmatematik | Computational Mathematics | Matematik

MSC 65M70 | Computational Mathematics and Numerical Analysis | RBF–PUM | Theoretical, Mathematical and Computational Physics | Convection–diffusion equation | Partition of unity | Mathematics | Meshfree | Radial basis function | MSC 35K15 | American option | Algorithms | Appl.Mathematics/Computational Methods of Engineering | Collocation method | STENCILS | MATHEMATICS, APPLIED | STOCHASTIC VOLATILITY | INTERPOLATION | PRICING AMERICAN OPTIONS | PENALTY METHODS | RBF-PUM | Convection-diffusion equation | Methods | Pricing | Naturvetenskap | Natural Sciences | Beräkningsmatematik | Computational Mathematics | Matematik

Journal Article

SIAM Journal on Scientific Computing, ISSN 1064-8275, 2006, Volume 27, Issue 3, pp. 1118 - 1139

Recently there has been a growing interest in designing efficient methods for the solution of ordinary/ partial differential equations with random inputs. To...

Collocation methods | Uncertainty quantification | Stochastic inputs | Differential equations | MONOMIAL CUBATURE RULES | CHAOS | MATHEMATICS, APPLIED | uncertainty quantification | STROUD | STOCHASTIC FINITE-ELEMENTS | MODELING UNCERTAINTY | stochastic inputs | SIMULATIONS | collocation methods | differential equations

Collocation methods | Uncertainty quantification | Stochastic inputs | Differential equations | MONOMIAL CUBATURE RULES | CHAOS | MATHEMATICS, APPLIED | uncertainty quantification | STROUD | STOCHASTIC FINITE-ELEMENTS | MODELING UNCERTAINTY | stochastic inputs | SIMULATIONS | collocation methods | differential equations

Journal Article

SIAM Journal on Scientific Computing, ISSN 1064-8275, 2014, Volume 36, Issue 2, pp. A495 - A521

We present a numerical method for utilizing stochastic models with differing fidelities to approximate parameterized functions. A representative case is where...

Multifidelity models | Nonintrusive stochastic collocation | Model-order reduction | MATHEMATICS, APPLIED | REDUCED BASIS METHOD | GREEDY ALGORITHMS | PARTIAL-DIFFERENTIAL-EQUATIONS | nonintrusive stochastic collocation | FEKETE | model-order reduction | OPTIMIZATION | POINTS | multifidelity models

Multifidelity models | Nonintrusive stochastic collocation | Model-order reduction | MATHEMATICS, APPLIED | REDUCED BASIS METHOD | GREEDY ALGORITHMS | PARTIAL-DIFFERENTIAL-EQUATIONS | nonintrusive stochastic collocation | FEKETE | model-order reduction | OPTIMIZATION | POINTS | multifidelity models

Journal Article

Journal of Computational Physics, ISSN 0021-9991, 2007, Volume 225, Issue 1, pp. 652 - 685

In recent years, there has been an interest in analyzing and quantifying the effects of random inputs in the solution of partial differential equations that...

Sparse grids | Navier–Stokes equations | Stochastic partial differential equations | Adaptive sampling | Collocation methods | Stochastic Galerkin method | Natural convection | Navier-Stokes equations | adaptive sampling | POLYNOMIAL CHAOS | Stochastic galerkin method | natural convection | DIFFERENTIAL-EQUATIONS | collocation methods | sparse grids | PHYSICS, MATHEMATICAL | FLOW | TRANSPORT | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | MODELING UNCERTAINTY | NavierStokes equations | Monte Carlo method | Analysis | Algorithms | RANDOMNESS | TOPOLOGY | STOCHASTIC PROCESSES | MONTE CARLO METHOD | CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS | ALGORITHMS | CHAOS THEORY | COMPUTERIZED SIMULATION | POLYNOMIALS | MATHEMATICAL SOLUTIONS | NATURAL CONVECTION | NAVIER-STOKES EQUATIONS | PHYSICAL PROPERTIES | CONVERGENCE | FLUID FLOW

Sparse grids | Navier–Stokes equations | Stochastic partial differential equations | Adaptive sampling | Collocation methods | Stochastic Galerkin method | Natural convection | Navier-Stokes equations | adaptive sampling | POLYNOMIAL CHAOS | Stochastic galerkin method | natural convection | DIFFERENTIAL-EQUATIONS | collocation methods | sparse grids | PHYSICS, MATHEMATICAL | FLOW | TRANSPORT | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | MODELING UNCERTAINTY | NavierStokes equations | Monte Carlo method | Analysis | Algorithms | RANDOMNESS | TOPOLOGY | STOCHASTIC PROCESSES | MONTE CARLO METHOD | CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS | ALGORITHMS | CHAOS THEORY | COMPUTERIZED SIMULATION | POLYNOMIALS | MATHEMATICAL SOLUTIONS | NATURAL CONVECTION | NAVIER-STOKES EQUATIONS | PHYSICAL PROPERTIES | CONVERGENCE | FLUID FLOW

Journal Article

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Adaptive multi‐index collocation for uncertainty quantification and sensitivity analysis

International Journal for Numerical Methods in Engineering, ISSN 0029-5981, 03/2020, Volume 121, Issue 6, pp. 1314 - 1343

Summary In this paper, we present an adaptive algorithm to construct response surface approximations of high‐fidelity models using a hierarchy of lower...

uncertainty quantification | modeling | multifidelity | sensitivity analysis | simulation | decision making | validation | POLYNOMIAL CHAOS | EXPANSIONS | STOCHASTIC COLLOCATION | INTERPOLATION | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | ENGINEERING, MULTIDISCIPLINARY | PARTIAL-DIFFERENTIAL-EQUATIONS | Accuracy | Uncertainty | Algorithms | Sensitivity analysis | Aerospace vehicles | Collocation | Computer simulation | Adaptive algorithms | Nozzles | Cost analysis | Response surfaces

uncertainty quantification | modeling | multifidelity | sensitivity analysis | simulation | decision making | validation | POLYNOMIAL CHAOS | EXPANSIONS | STOCHASTIC COLLOCATION | INTERPOLATION | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | ENGINEERING, MULTIDISCIPLINARY | PARTIAL-DIFFERENTIAL-EQUATIONS | Accuracy | Uncertainty | Algorithms | Sensitivity analysis | Aerospace vehicles | Collocation | Computer simulation | Adaptive algorithms | Nozzles | Cost analysis | Response surfaces

Journal Article

Computer Methods in Applied Mechanics and Engineering, ISSN 0045-7825, 07/2016, Volume 306, pp. 95 - 122

In this work we introduce the Multi-Index Stochastic Collocation method (MISC) for computing statistics of the solution of a PDE with random data. MISC is a...

Uncertainty Quantification | Random PDEs | Sparse grids | Stochastic Collocation methods | Combination technique | Multilevel methods | RANDOM INPUT DATA | MONTE-CARLO METHODS | APPROXIMATION | CLENSHAW-CURTIS | ALGORITHMS | ELLIPTIC PDES | RANDOM-COEFFICIENTS | CHAOS | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | MECHANICS | ENGINEERING, MULTIDISCIPLINARY | PARTIAL-DIFFERENTIAL-EQUATIONS | Construction | Collocation | Computation | Multilevel | Collocation methods | Mathematical models | Stochasticity | Optimization | Mathematics - Numerical Analysis

Uncertainty Quantification | Random PDEs | Sparse grids | Stochastic Collocation methods | Combination technique | Multilevel methods | RANDOM INPUT DATA | MONTE-CARLO METHODS | APPROXIMATION | CLENSHAW-CURTIS | ALGORITHMS | ELLIPTIC PDES | RANDOM-COEFFICIENTS | CHAOS | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | MECHANICS | ENGINEERING, MULTIDISCIPLINARY | PARTIAL-DIFFERENTIAL-EQUATIONS | Construction | Collocation | Computation | Multilevel | Collocation methods | Mathematical models | Stochasticity | Optimization | Mathematics - Numerical Analysis

Journal Article

Journal of Computational Physics, ISSN 0021-9991, 04/2016, Volume 310, pp. 301 - 328

The Simplex-Stochastic Collocation (SSC) method is a robust tool used to propagate uncertain input distributions through a computer code. However, it becomes...

High-dimensional model reduction techniques | Uncertainty quantification | Surrogate model | Simplex-stochastic collocation method | Uniform simplex sampling | RANDOM INPUT DATA | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | PARTIAL-DIFFERENTIAL-EQUATIONS | TURBULENCE MODEL | PHYSICS, MATHEMATICAL | MODEL REPRESENTATIONS | Analysis | Methods | Aerospace engineering | Interpolation | Collocation | Computation | Mathematical analysis | Collocation methods | Sampling | Computer programs | Engineering Sciences | INTERPOLATION | SAMPLING | DISTRIBUTION | STOCHASTIC PROCESSES | MAPS | CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS | MATRICES | COMPUTER CODES

High-dimensional model reduction techniques | Uncertainty quantification | Surrogate model | Simplex-stochastic collocation method | Uniform simplex sampling | RANDOM INPUT DATA | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | PARTIAL-DIFFERENTIAL-EQUATIONS | TURBULENCE MODEL | PHYSICS, MATHEMATICAL | MODEL REPRESENTATIONS | Analysis | Methods | Aerospace engineering | Interpolation | Collocation | Computation | Mathematical analysis | Collocation methods | Sampling | Computer programs | Engineering Sciences | INTERPOLATION | SAMPLING | DISTRIBUTION | STOCHASTIC PROCESSES | MAPS | CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS | MATRICES | COMPUTER CODES

Journal Article

Journal of Computational Physics, ISSN 0021-9991, 2008, Volume 227, Issue 22, pp. 9572 - 9595

Stochastic spectral methods are numerical techniques for approximating solutions to partial differential equations with random parameters. In this work, we...

Sparse grids | Domain decomposition | Stochastic partial differential equations | ELLIPTIC PROBLEMS | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | PARTIAL-DIFFERENTIAL-EQUATIONS | APPROXIMATIONS | GENERALIZED POLYNOMIAL CHAOS | STOCHASTIC COEFFICIENTS | PHYSICS, MATHEMATICAL | Monte Carlo method | Methods | Algorithms | RANDOMNESS | TENSORS | ERRORS | STOCHASTIC PROCESSES | MONTE CARLO METHOD | CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS | QUADRATURES | ALGORITHMS | TWO-DIMENSIONAL CALCULATIONS | MATHEMATICAL SOLUTIONS | NAVIER-STOKES EQUATIONS | PROBABILISTIC ESTIMATION | CONVERGENCE | DIFFUSION

Sparse grids | Domain decomposition | Stochastic partial differential equations | ELLIPTIC PROBLEMS | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | PARTIAL-DIFFERENTIAL-EQUATIONS | APPROXIMATIONS | GENERALIZED POLYNOMIAL CHAOS | STOCHASTIC COEFFICIENTS | PHYSICS, MATHEMATICAL | Monte Carlo method | Methods | Algorithms | RANDOMNESS | TENSORS | ERRORS | STOCHASTIC PROCESSES | MONTE CARLO METHOD | CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS | QUADRATURES | ALGORITHMS | TWO-DIMENSIONAL CALCULATIONS | MATHEMATICAL SOLUTIONS | NAVIER-STOKES EQUATIONS | PROBABILISTIC ESTIMATION | CONVERGENCE | DIFFUSION

Journal Article

SIAM Journal on Numerical Analysis, ISSN 0036-1429, 1/2008, Volume 46, Issue 5, pp. 2309 - 2345

This work proposes and analyzes a Smolyak-type sparse grid stochastic collocation method for the approximation of statistical quantities related to the...

Interpolation | Error rates | Tensors | Monte Carlo methods | Approximation | Input data | Textual collocation | Polynomials | Random variables | Coefficients | Finite elements | Sparse grids | Multivariate polynomial approximation | Stochastic PDEs | Uncertainty quantification | Collocation techniques | Smolyak approximation | INTERPOLATION | MATHEMATICS, APPLIED | collocation techniques | stochastic PDEs | uncertainty quantification | UNCERTAINTY | QUADRATURE | sparse grids | finite elements | multivariate polynomial approximation | Data- och informationsvetenskap | quadrature | quantification | multivariate | Computer and Information Sciences | uncertainty | polynomial chaos | interpolation | Naturvetenskap | polynomial approximation | coefficients | Natural Sciences

Interpolation | Error rates | Tensors | Monte Carlo methods | Approximation | Input data | Textual collocation | Polynomials | Random variables | Coefficients | Finite elements | Sparse grids | Multivariate polynomial approximation | Stochastic PDEs | Uncertainty quantification | Collocation techniques | Smolyak approximation | INTERPOLATION | MATHEMATICS, APPLIED | collocation techniques | stochastic PDEs | uncertainty quantification | UNCERTAINTY | QUADRATURE | sparse grids | finite elements | multivariate polynomial approximation | Data- och informationsvetenskap | quadrature | quantification | multivariate | Computer and Information Sciences | uncertainty | polynomial chaos | interpolation | Naturvetenskap | polynomial approximation | coefficients | Natural Sciences

Journal Article

Journal of Computational Physics, ISSN 0021-9991, 2010, Volume 229, Issue 5, pp. 1536 - 1557

We combine multi-element polynomial chaos with analysis of variance (ANOVA) functional decomposition to enhance the convergence rate of polynomial chaos in...

Sparse grids | Domain decomposition | Stochastic partial differential equations | ELLIPTIC PROBLEMS | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | PARTIAL-DIFFERENTIAL-EQUATIONS | APPROXIMATIONS | GENERALIZED POLYNOMIAL CHAOS | ERROR | ALGORITHMS | PHYSICS, MATHEMATICAL | EFFICIENT | Radioactive waste sites | Radioactive wastes | Hydrogeology | Analysis | Methods | POLYNOMIALS | STOCHASTIC PROCESSES | MONTE CARLO METHOD | CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS | HYDRAULIC CONDUCTIVITY | PARTIAL DIFFERENTIAL EQUATIONS | PROBABILISTIC ESTIMATION | RADIOACTIVE WASTES | CHAOS THEORY

Sparse grids | Domain decomposition | Stochastic partial differential equations | ELLIPTIC PROBLEMS | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | PARTIAL-DIFFERENTIAL-EQUATIONS | APPROXIMATIONS | GENERALIZED POLYNOMIAL CHAOS | ERROR | ALGORITHMS | PHYSICS, MATHEMATICAL | EFFICIENT | Radioactive waste sites | Radioactive wastes | Hydrogeology | Analysis | Methods | POLYNOMIALS | STOCHASTIC PROCESSES | MONTE CARLO METHOD | CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS | HYDRAULIC CONDUCTIVITY | PARTIAL DIFFERENTIAL EQUATIONS | PROBABILISTIC ESTIMATION | RADIOACTIVE WASTES | CHAOS THEORY

Journal Article

Journal of Computational Physics, ISSN 0021-9991, 2009, Volume 228, Issue 8, pp. 3084 - 3113

In recent years, there has been a growing interest in analyzing and quantifying the effects of random inputs in the solution of ordinary/partial differential...

Stochastic partial differential equations | Sparse grid | Collocation | Hierarchical multiscale method | Discontinuities | Adaptive sparse grid | Smolyak algorithm | NATURAL-CONVECTION | SENSITIVITY | GENERALIZED POLYNOMIAL CHAOS | PHYSICS, MATHEMATICAL | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | MODELING UNCERTAINTY | Monte Carlo method | Mechanical engineering | Algorithms | Discontinuity | Monte Carlo methods | Mathematical analysis | Collocation methods | Mathematical models | Stochasticity | INTERPOLATION | POLYNOMIALS | RANDOMNESS | STOCHASTIC PROCESSES | MONTE CARLO METHOD | CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS | PARTIAL DIFFERENTIAL EQUATIONS | DECOMPOSITION | CONVERGENCE | ALGORITHMS | COMPARATIVE EVALUATIONS | FINITE ELEMENT METHOD

Stochastic partial differential equations | Sparse grid | Collocation | Hierarchical multiscale method | Discontinuities | Adaptive sparse grid | Smolyak algorithm | NATURAL-CONVECTION | SENSITIVITY | GENERALIZED POLYNOMIAL CHAOS | PHYSICS, MATHEMATICAL | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | MODELING UNCERTAINTY | Monte Carlo method | Mechanical engineering | Algorithms | Discontinuity | Monte Carlo methods | Mathematical analysis | Collocation methods | Mathematical models | Stochasticity | INTERPOLATION | POLYNOMIALS | RANDOMNESS | STOCHASTIC PROCESSES | MONTE CARLO METHOD | CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS | PARTIAL DIFFERENTIAL EQUATIONS | DECOMPOSITION | CONVERGENCE | ALGORITHMS | COMPARATIVE EVALUATIONS | FINITE ELEMENT METHOD

Journal Article

SIAM Journal on Numerical Analysis, ISSN 0036-1429, 1/2007, Volume 45, Issue 3, pp. 1005 - 1034

In this paper we propose and analyze a stochastic collocation method to solve elliptic partial differential equations with random coefficients and forcing...

Interpolation | Tensors | Approximation | Input data | Textual collocation | Polynomials | Mathematical moments | Random variables | Diffusion coefficient | Coefficients | Uncertainty quantification | Exponential convergence | Finite elements | Stochastic partial differential equations | Collocation method | collocation method | INTERPOLATION | MATHEMATICS, APPLIED | uncertainty quantification | POLYNOMIAL CHAOS | stochastic partial differential equations | UNCERTAINTY | COEFFICIENTS | ALGORITHMS | exponential convergence | FINITE-ELEMENT-METHOD | finite elements | Data- och informationsvetenskap | algorithms | finite-element-method | Computer and Information Sciences | uncertainty | polynomial chaos | interpolation | Naturvetenskap | coefficients | Natural Sciences

Interpolation | Tensors | Approximation | Input data | Textual collocation | Polynomials | Mathematical moments | Random variables | Diffusion coefficient | Coefficients | Uncertainty quantification | Exponential convergence | Finite elements | Stochastic partial differential equations | Collocation method | collocation method | INTERPOLATION | MATHEMATICS, APPLIED | uncertainty quantification | POLYNOMIAL CHAOS | stochastic partial differential equations | UNCERTAINTY | COEFFICIENTS | ALGORITHMS | exponential convergence | FINITE-ELEMENT-METHOD | finite elements | Data- och informationsvetenskap | algorithms | finite-element-method | Computer and Information Sciences | uncertainty | polynomial chaos | interpolation | Naturvetenskap | coefficients | Natural Sciences

Journal Article

Communications in Computational Physics, ISSN 1815-2406, 07/2015, Volume 18, Issue 1, pp. 1 - 36

Collocation has become a standard tool for approximation of parameterized systems in the uncertainty quantification (UQ) community. Techniques for...

least interpolation | least-squares | Stochastic collocation | compressive sampling | unstructured methes | LEBESGUE FUNCTIONS | POLYNOMIAL INTERPOLATION | APPROXIMATIONS | SIGNAL RECOVERY | ALGORITHMS | PHYSICS, MATHEMATICAL | CHAOS | FEKETE POINTS | PARTIAL-DIFFERENTIAL-EQUATIONS | UNCERTAINTY | CONSTRUCTIONS

least interpolation | least-squares | Stochastic collocation | compressive sampling | unstructured methes | LEBESGUE FUNCTIONS | POLYNOMIAL INTERPOLATION | APPROXIMATIONS | SIGNAL RECOVERY | ALGORITHMS | PHYSICS, MATHEMATICAL | CHAOS | FEKETE POINTS | PARTIAL-DIFFERENTIAL-EQUATIONS | UNCERTAINTY | CONSTRUCTIONS

Journal Article

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A collocation technique for solving nonlinear Stochastic Itô–Volterra integral equations

Applied Mathematics and Computation, ISSN 0096-3003, 11/2014, Volume 247, pp. 1011 - 1020

A numerical method for solving nonlinear Stochastic Itô–Volterra equations is proposed. The method is based on delta function (DF) approximations. The...

Delta functions | Error analysis | Stochastic | Collocation | Vector forms | Operational matrices | MATHEMATICS, APPLIED | NUMERICAL-SOLUTION | RANDOM DIFFERENTIAL-EQUATIONS | INTEGRODIFFERENTIAL EQUATIONS | Approximation | Computation | Mathematical analysis | Nonlinearity | Mathematical models | Stochasticity | Dynamical systems | Delta function

Delta functions | Error analysis | Stochastic | Collocation | Vector forms | Operational matrices | MATHEMATICS, APPLIED | NUMERICAL-SOLUTION | RANDOM DIFFERENTIAL-EQUATIONS | INTEGRODIFFERENTIAL EQUATIONS | Approximation | Computation | Mathematical analysis | Nonlinearity | Mathematical models | Stochasticity | Dynamical systems | Delta function

Journal Article

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A Christoffel function weighted least squares algorithm for collocation approximations

Mathematics of Computation of the American Mathematical Society, ISSN 0025-5718, 07/2017, Volume 86, Issue 306, pp. 1913 - 1947

We propose, theoretically investigate, and numerically validate an algorithm for the Monte Carlo solution of least-squares polynomial approximation problems in...

PROJECTION | CHAOS | MATHEMATICS, APPLIED | MARKOV-TYPE | POLYNOMIAL-APPROXIMATION | BERGMAN KERNELS | EQUILIBRIUM MEASURES | ASYMPTOTICS | STOCHASTIC COLLOCATION | MATHEMATICS AND COMPUTING

PROJECTION | CHAOS | MATHEMATICS, APPLIED | MARKOV-TYPE | POLYNOMIAL-APPROXIMATION | BERGMAN KERNELS | EQUILIBRIUM MEASURES | ASYMPTOTICS | STOCHASTIC COLLOCATION | MATHEMATICS AND COMPUTING

Journal Article

Journal of Hydrology, ISSN 0022-1694, 09/2016, Volume 540, pp. 488 - 503

•Worth of dynamic data to characterize solute transport in heterogeneous aquifers.•Sequential assessment of worth of dynamic data to design monitoring...

Probabilistic collocation | Data worth | Contaminant migration | Ensemble Kalman Filter | POLYNOMIAL CHAOS EXPANSION | UNCERTAINTY QUANTIFICATION | WATER RESOURCES | FLOW | GLOBAL SENSITIVITY-ANALYSIS | ENGINEERING, CIVIL | GEOSCIENCES, MULTIDISCIPLINARY | STOCHASTIC MOMENT EQUATIONS | DATA ASSIMILATION | EXPERIMENTAL-DESIGN | EFFICIENT | BAYESIAN-INFERENCE | GROUNDWATER | Analysis | Aquifers | Hydrology | Permeability | Reduction | Contaminants | Mathematical models | Computational efficiency | Kalman filters | Dynamical systems | Plumes

Probabilistic collocation | Data worth | Contaminant migration | Ensemble Kalman Filter | POLYNOMIAL CHAOS EXPANSION | UNCERTAINTY QUANTIFICATION | WATER RESOURCES | FLOW | GLOBAL SENSITIVITY-ANALYSIS | ENGINEERING, CIVIL | GEOSCIENCES, MULTIDISCIPLINARY | STOCHASTIC MOMENT EQUATIONS | DATA ASSIMILATION | EXPERIMENTAL-DESIGN | EFFICIENT | BAYESIAN-INFERENCE | GROUNDWATER | Analysis | Aquifers | Hydrology | Permeability | Reduction | Contaminants | Mathematical models | Computational efficiency | Kalman filters | Dynamical systems | Plumes

Journal Article