Constructing Surrogate Models of Complex Systems with Enhanced Sparsity: Quantifying the Influence of Conformational Uncertainty in Biomolecular Solvation
From MaRDI portal
Publication:3459658
DOI10.1137/140981587zbMath1339.92024arXiv1408.5629OpenAlexW2618422357WikidataQ36449330 ScholiaQ36449330MaRDI QIDQ3459658
No author found.
Publication date: 11 January 2016
Published in: Multiscale Modeling & Simulation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1408.5629
model reductionuncertainty quantificationpolynomial chaosbiomolecular conformation fluctuationcompressive sensing method
Statistical mechanics of polymers (82D60) Biochemistry, molecular biology (92C40) Molecular structure (graph-theoretic methods, methods of differential topology, etc.) (92E10) Applications of statistical mechanics to specific types of physical systems (82D99)
Related Items
Data-driven molecular modeling with the generalized Langevin equation ⋮ Adaptive deep density approximation for Fokker-Planck equations ⋮ A General Framework of Rotational Sparse Approximation in Uncertainty Quantification ⋮ A bi-fidelity surrogate modeling approach for uncertainty propagation in three-dimensional hemodynamic simulations ⋮ A locally conservative multiscale method for stochastic highly heterogeneous flow ⋮ Sliced-Inverse-Regression--Aided Rotated Compressive Sensing Method for Uncertainty Quantification ⋮ Calibration of reduced-order model for a coupled Burgers equations based on PC-EnKF ⋮ Sparsity-promoting elastic net method with rotations for high-dimensional nonlinear inverse problem ⋮ A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness ⋮ An adaptive WENO collocation method for differential equations with random coefficients ⋮ Enhancing sparsity of Hermite polynomial expansions by iterative rotations ⋮ Rank adaptive tensor recovery based model reduction for partial differential equations with high-dimensional random inputs
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Reweighted \(\ell_1\) minimization method for stochastic elliptic differential equations
- Basis adaptation in homogeneous chaos spaces
- Sparse Legendre expansions via \(\ell_1\)-minimization
- Adaptive ANOVA decomposition of stochastic incompressible and compressible flows
- A non-adapted sparse approximation of PDEs with stochastic inputs
- Multi-element probabilistic collocation method in high dimensions
- The restricted isometry property and its implications for compressed sensing
- The multi-element probabilistic collocation method (ME-PCM): Error analysis and applications
- An adaptive high-dimensional stochastic model representation technique for the solution of stochastic partial differential equations
- An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations
- Fully symmetric interpolatory rules for multiple integrals over infinite regions with Gaussian weight
- Sparse grid collocation schemes for stochastic natural convection problems
- Constructing Nested Nodal Sets for Multivariate Polynomial Interpolation
- Error Estimates for the ANOVA Method with Polynomial Chaos Interpolation: Tensor Product Functions
- Stochastic Collocation Methods on Unstructured Grids in High Dimensions via Interpolation
- Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces
- A Recursive Sparse Grid Collocation Method for Differential Equations with White Noise
- Linearized Bregman iterations for compressed sensing
- Convergence of the linearized Bregman iteration for ℓ₁-norm minimization
- The Split Bregman Method for L1-Regularized Problems
- Decoding by Linear Programming
- Stable recovery of sparse overcomplete representations in the presence of noise
- Probing the Pareto Frontier for Basis Pursuit Solutions
- From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
- An Anisotropic Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data
- The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
- Uncertainty Quantification in MD Simulations. Part I: Forward Propagation
- Uncertainty Quantification in MD Simulations. Part II: Bayesian Inference of Force-Field Parameters
- STOCHASTIC COLLOCATION ALGORITHMS USING l1-MINIMIZATION
- Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
- High-Order Collocation Methods for Differential Equations with Random Inputs
