Using automatic differentiation for compressive sensing in uncertainty quantification
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Publication:4685570
DOI10.1080/10556788.2017.1359267zbMath1453.94032OpenAlexW2744147826MaRDI QIDQ4685570
Alex Pothen, Guang Lin, Mu Wang
Publication date: 9 October 2018
Published in: Optimization Methods and Software (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10556788.2017.1359267
automatic differentiationHermite polynomialsuncertainty quantificationcompressive sensingreverse modegeneralized polynomial chaos expansion
Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Probabilistic methods, stochastic differential equations (65C99)
Uses Software
Cites Work
- Reweighted \(\ell_1\) minimization method for stochastic elliptic differential equations
- A weighted \(\ell_1\)-minimization approach for sparse polynomial chaos expansions
- A non-adapted sparse approximation of PDEs with stochastic inputs
- A Bayesian mixed shrinkage prior procedure for spatial-stochastic basis selection and evaluation of gPC expansions: applications to elliptic SPDEs
- Enhancing \(\ell_1\)-minimization estimates of polynomial chaos expansions using basis selection
- Enhancing sparsity of Hermite polynomial expansions by iterative rotations
- Enhancing sparsity by reweighted \(\ell _{1}\) minimization
- On polynomial chaos expansion via gradient-enhanced \(\ell_1\)-minimization
- Active Subspaces
- The Art of Differentiating Computer Programs
- Evaluating Derivatives
- From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
- The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
- DIMENSIONALITY REDUCTION FOR COMPLEX MODELS VIA BAYESIAN COMPRESSIVE SENSING
- High-Order Collocation Methods for Differential Equations with Random Inputs
- A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data
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