Regression-based sparse polynomial chaos for uncertainty quantification of subsurface flow models
نویسندگان
چکیده
منابع مشابه
Adaptive sparse polynomial chaos expansion based on least angle regression
Polynomial chaos (PC) expansions are used in stochastic finite element analysis to represent the random model response by a set of coefficients in a suitable (so-called polynomial chaos) basis. The number of terms to be computed grows dramatically with the size of the input random vector, which makes the computational cost of classical solution schemes (may it be intrusive (i.e. of Galerkin typ...
متن کاملAdaptive Sparse Grid Approaches to Polynomial Chaos Expansions for Uncertainty Quantification
Adaptive Sparse Grid Approaches to Polynomial Chaos Expansions for Uncertainty Quantification by Justin Gregory Winokur Department of Mechanical Engineering & Materials Science Duke University Date: Approved: Omar M. Knio, Supervisor
متن کاملEfficient Uncertainty Quantification with Polynomial Chaos for Implicit Stiff Systems
The polynomial chaos method has been widely adopted as a computationally feasible approach for uncertainty quantification. Most studies to date have focused on non-stiff systems. When stiff systems are considered, implicit numerical integration requires the solution of a nonlinear system of equations at every time step. Using the Galerkin approach, the size of the system state increases from n ...
متن کاملUncertainty Propagation in Puff-based Dispersion Models Using Polynomial Chaos
Atmospheric dispersion is a complex nonlinear physical process with numerous uncertainties in model parameters, inputs, source parameters, initial and boundary conditions. Accurate propagation of these uncertainties through the dispersion models is crucial for a reliable prediction of the probability distribution of the states and assessment of risk. A simple three-dimensional Gaussian puff-bas...
متن کاملData-driven uncertainty quantification using the arbitrary polynomial chaos expansion
We discuss the arbitrary polynomial chaos (aPC), which has been subject of research in a few recent theoretical papers. Like all polynomial chaos expansion techniques, aPC approximates the dependence of simulation model output on model parameters by expansion in an orthogonal polynomial basis. The aPC generalizes chaos expansion techniques towards arbitrary distributions with arbitrary probabil...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computational Physics
سال: 2019
ISSN: 0021-9991
DOI: 10.1016/j.jcp.2019.108909