Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification

Cites Work

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Multi-output local Gaussian process regression: applications to uncertainty quantification
A non-adapted sparse approximation of PDEs with stochastic inputs
Adaptive sparse polynomial chaos expansion based on least angle regression
Sparse pseudospectral approximation method
Gaussian processes with built-in dimensionality reduction: applications to high-dimensional uncertainty propagation
Data-driven probability concentration and sampling on manifold
Enhancing \(\ell_1\)-minimization estimates of polynomial chaos expansions using basis selection
Asymptotic behaviour of the stochastic Lotka-Volterra model.
Riemannian geometry of Grassmann manifolds with a view on algorithmic computation
Non-intrusive reduced order modeling of nonlinear problems using neural networks
Uncertainty quantification for complex systems with very high dimensional response using Grassmann manifold variations
Polynomial chaos representation of databases on manifolds
A polynomial chaos expansion in dependent random variables
On conjugate gradient-like methods for eigen-like problems
Sparse polynomial chaos expansions via compressed sensing and D-optimal design
Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification
Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold
Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network
Data-driven uncertainty quantification in computational human head models
Polynomial chaos expansions for dependent random variables
Global sensitivity analysis for multivariate outputs using polynomial chaos-based surrogate models
Compressive sensing adaptation for polynomial chaos expansions
Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets
Least squares polynomial chaos expansion: a review of sampling strategies
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Data-driven uncertainty quantification for predictive flow and transport modeling using support vector machines
Low-rank matrix completion via preconditioned optimization on the Grassmann manifold
Sparse pseudo spectral projection methods with directional adaptation for uncertainty quantification
Basis adaptive sample efficient polynomial chaos (BASE-PC)
Diffusion maps
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems
On the influence of over-parameterization in manifold based surrogates and deep neural operators
Schubert varieties and distances between subspaces of different dimensions
Introduction to Uncertainty Quantification
Multi-Element Generalized Polynomial Chaos for Arbitrary Probability Measures
Riemannian center of mass and mollifier smoothing
The Geometry of Algorithms with Orthogonality Constraints
Multivariate Polynomial Chaos Expansions with Dependent Variables
Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure
The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark
EXTENDING CLASSICAL SURROGATE MODELING TO HIGH DIMENSIONS THROUGH SUPERVISED DIMENSIONALITY REDUCTION: A DATA-DRIVEN APPROACH
Automatic selection of basis-adaptive sparse polynomial chaos expansions for engineering applications
Grassmannian Diffusion Maps--Based Dimension Reduction and Classification for High-Dimensional Data
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
Adaptive Smolyak Pseudospectral Approximations
Uncertainty propagation in CFD using polynomial chaos decomposition
Introduction to Riemannian Geometry and Geometric Statistics: From Basic Theory to Implementation with Geomstats
Monte Carlo strategies in scientific computing
Grassmannian diffusion maps based surrogate modeling via geometric harmonics
Diffusion maps-based surrogate modeling: an alternative machine learning approach
Variance-based simplex stochastic collocation with model order reduction for high-dimensional systems

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