Gaussian Process Subspace Prediction for Model Reduction

DOI10.1137/21M1432739zbMath1492.60090arXiv2107.04668OpenAlexW4281669620WikidataQ114074010 ScholiaQ114074010MaRDI QIDQ5864693

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Publication date: 8 June 2022

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2107.04668

zbMATH Keywords

subspaceGaussian processGrassmann manifoldreduced order modelinguncertainty quantificationparameter adaptation

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Gaussian processes (60G15) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (2)

DRIPS: a framework for dimension reduction and interpolation in parameter space ⋮ Sliced Gradient-Enhanced Kriging for High-Dimensional Function Approximation

Uses Software

• MATLAB expm
• MESS

Cites Work

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• Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
• Fréchet regression for random objects with Euclidean predictors
• Bayesian manifold regression
• Interpolatory weighted-\(\mathcal{H}_2\) model reduction
• Efficient emulators of computer experiments using compactly supported correlation functions, with an application to cosmology
• The design and analysis of computer experiments
• Statistics on special manifolds
• Extrinsic Gaussian processes for regression and classification on manifolds
• Geodesic finite elements of higher order
• A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
• Schubert Varieties and Distances between Subspaces of Different Dimensions
• Quasi-interpolation in Riemannian manifolds
• Active Subspaces
• A real time procedure for affinely dependent parametric model order reduction using interpolation on Grassmann manifolds
• Nonlinear Model Reduction via Discrete Empirical Interpolation
• Dynamic mode decomposition of numerical and experimental data
• Riemannian $L^{p}$ center of mass: Existence, uniqueness, and convexity
• An Online Method for Interpolating Linear Parametric Reduced-Order Models
• An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy Simulations
• $\mathcal{H}_2$ Model Reduction for Large-Scale Linear Dynamical Systems
• Principal component analysis in linear systems: Controllability, observability, and model reduction
• The Geometry of Algorithms with Orthogonality Constraints
• Normal-Bundle Bootstrap
• Hermite Interpolation and Data Processing Errors on Riemannian Matrix Manifolds
• An observation-driven time-dependent basis for a reduced description of transient stochastic systems
• An "$hp$" Certified Reduced Basis Method for Parametrized Elliptic Partial Differential Equations
• Intrinsic Gaussian Processes on Complex Constrained Domains
• The Scaling and Squaring Method for the Matrix Exponential Revisited

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