Pages that link to "Item:Q2952708"

The following pages link to Data-driven model reduction for the Bayesian solution of inverse problems (Q2952708):

Displaying 50 items.

• Multiscale model reduction method for Bayesian inverse problems of subsurface flow (Q515766) (← links)
• Gaussian process regression and conditional polynomial chaos for parameter estimation (Q781985) (← links)
• Data-driven combined state and parameter reduction for inverse problems (Q904256) (← links)
• Dimensionality reduction and polynomial chaos acceleration of Bayesian inference in inverse problems (Q1009939) (← links)
• Fast model updating coupling Bayesian inference and PGD model reduction (Q1628759) (← links)
• Reduced modeling of unknown trajectories (Q1639588) (← links)
• Multimodal, high-dimensional, model-based, Bayesian inverse problems with applications in biomechanics (Q1714421) (← links)
• Local search methods for the solution of implicit inverse problems (Q1800277) (← links)
• An adaptive local reduced basis method for solving PDEs with uncertain inputs and evaluating risk (Q1986786) (← links)
• Accelerating MCMC via Kriging-based adaptive independent proposals and delayed rejection (Q1988268) (← links)
• Randomized reduced forward models for efficient Metropolis-Hastings MCMC, with application to subsurface fluid flow and capacitance tomography (Q2023287) (← links)
• A model reduction approach for inverse problems with operator valued data (Q2049926) (← links)
• Accelerating the Bayesian inference of inverse problems by using data-driven compressive sensing method based on proper orthogonal decomposition (Q2055173) (← links)
• Polynomial surrogates for Bayesian traveltime tomography (Q2062365) (← links)
• Low-rank dynamic mode decomposition: an exact and tractable solution (Q2062877) (← links)
• Objective-sensitive principal component analysis for high-dimensional inverse problems (Q2065835) (← links)
• Consistency analysis of bilevel data-driven learning in inverse problems (Q2076194) (← links)
• Variational inference for nonlinear inverse problems via neural net kernels: comparison to Bayesian neural networks, application to topology optimization (Q2083125) (← links)
• A generalized probabilistic learning approach for multi-fidelity uncertainty quantification in complex physical simulations (Q2083198) (← links)
• Deep composition of tensor-trains using squared inverse Rosenblatt transports (Q2098237) (← links)
• Multi-frequency model reduction for uncertainty quantification in computational vibroacoutics (Q2115601) (← links)
• Nonlinear sparse Bayesian learning for physics-based models (Q2126972) (← links)
• Randomized approaches to accelerate MCMC algorithms for Bayesian inverse problems (Q2129320) (← links)
• Bayesian model inversion using stochastic spectral embedding (Q2131057) (← links)
• Physics constrained learning for data-driven inverse modeling from sparse observations (Q2135255) (← links)
• An adaptive SVD-Krylov reduced order model for surrogate based structural shape optimization through isogeometric boundary element method (Q2174142) (← links)
• Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems (Q2214560) (← links)
• Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion (Q2214630) (← links)
• Bayesian inversion for steady flow in fractured porous media with contact on fractures and hydro-mechanical coupling (Q2221173) (← links)
• An adaptive reduced basis ANOVA method for high-dimensional Bayesian inverse problems (Q2222423) (← links)
• A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the small data regime (Q2222510) (← links)
• Bayesian approach to inverse time-harmonic acoustic obstacle scattering with phaseless data generated by point source waves (Q2246258) (← links)
• A non-intrusive reduced basis EKI for time fractional diffusion inverse problems (Q2300550) (← links)
• A transport-based multifidelity preconditioner for Markov chain Monte Carlo (Q2305532) (← links)
• Adaptive reduced-basis generation for reduced-order modeling for the solution of stochastic nondestructive evaluation problems (Q2310309) (← links)
• Data completion and stochastic algorithms for PDE inversion problems with many measurements (Q2341368) (← links)
• Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction (Q2375191) (← links)
• Sparse-grid, reduced-basis Bayesian inversion: nonaffine-parametric nonlinear equations (Q2375242) (← links)
• Multilevel model reduction for uncertainty quantification in computational structural dynamics (Q2400060) (← links)
• Sparse-grid, reduced-basis Bayesian inversion (Q2631530) (← links)
• An iterative algorithm for POD basis adaptation in solving parametric convection-diffusion equations (Q2670330) (← links)
• Adaptive Sparse Grid Model Order Reduction for Fast Bayesian Estimation and Inversion (Q2808015) (← links)
• Optimal model management for multifidelity Monte Carlo estimation (Q2827043) (← links)
• Reduced-order model tracking and interpolation to solve PDE-based Bayesian inverse problems (Q2861886) (← links)
• Proper Generalized Decomposition model reduction in the Bayesian framework for solving inverse heat transfer problems (Q2974009) (← links)
• Parameter estimation with model order reduction for elliptic differential equations (Q4563456) (← links)
• Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization (Q4580293) (← links)
• The cardiovascular system: Mathematical modelling, numerical algorithms and clinical applications (Q4594245) (← links)
• A Hybrid Adaptive MCMC Algorithm in Function Spaces (Q4636400) (← links)
• Reduced Basis Methods for Uncertainty Quantification (Q4636408) (← links)