Pages that link to "Item:Q2952708"
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The following pages link to Data-driven model reduction for the Bayesian solution of inverse problems (Q2952708):
Displaying 33 items.
- Efficient State/Parameter Estimation in Nonlinear Unsteady PDEs by a Reduced Basis Ensemble Kalman Filter (Q4636410) (← links)
- Magnetic Resonance Electrical Impedance Tomography (MREIT): Convergence and Reduced Basis Approach (Q4686938) (← links)
- Multifidelity Approximate Bayesian Computation (Q4960985) (← links)
- (Q4969066) (← links)
- Data-driven forward discretizations for Bayesian inversion (Q4970560) (← links)
- A Multifidelity Ensemble Kalman Filter with Reduced Order Control Variates (Q4997361) (← links)
- Stein Variational Reduced Basis Bayesian Inversion (Q4997362) (← links)
- An Acceleration Strategy for Randomize-Then-Optimize Sampling Via Deep Neural Networks (Q5079536) (← links)
- Certified dimension reduction in nonlinear Bayesian inverse problems (Q5082037) (← links)
- Bayesian Mesh Adaptation for Estimating Distributed Parameters (Q5147977) (← links)
- Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media (Q5228359) (← links)
- Stochastic Collocation Algorithms Using $l_1$-Minimization for Bayesian Solution of Inverse Problems (Q5254808) (← links)
- Non‐linear model reduction for uncertainty quantification in large‐scale inverse problems (Q5306460) (← links)
- Convergence of spectral likelihood approximation based on q-Hermite polynomials for Bayesian inverse problems (Q5869782) (← links)
- Learning physics-based models from data: perspectives from inverse problems and model reduction (Q5887831) (← links)
- Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression (Q6048987) (← links)
- VI-DGP: a variational inference method with deep generative prior for solving high-dimensional inverse problems (Q6053024) (← links)
- Numerical linear algebra in data assimilation (Q6068266) (← links)
- Geometric learning for computational mechanics. III: Physics-constrained response surface of geometrically nonlinear shells (Q6096461) (← links)
- Context-Aware Surrogate Modeling for Balancing Approximation and Sampling Costs in Multifidelity Importance Sampling and Bayesian Inverse Problems (Q6109165) (← links)
- Reduced-order model-based variational inference with normalizing flows for Bayesian elliptic inverse problems (Q6145183) (← links)
- A greedy sensor selection algorithm for hyperparameterized linear Bayesian inverse problems with correlated noise models (Q6146995) (← links)
- Residual-based error correction for neural operator accelerated Infinite-dimensional Bayesian inverse problems (Q6147083) (← links)
- Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction (Q6158090) (← links)
- A data-driven and model-based accelerated Hamiltonian Monte Carlo method for Bayesian elliptic inverse problems (Q6172145) (← links)
- Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events (Q6189161) (← links)
- A low-rank solver for parameter estimation and uncertainty quantification in time-dependent systems of partial differential equations (Q6200967) (← links)
- Bayesian inference with subset simulation in varying dimensions applied to the Karhunen-Loève expansion (Q6554109) (← links)
- A posteriori stochastic correction of reduced models in delayed-acceptance MCMC, with application to multiphase subsurface inverse problems (Q6555345) (← links)
- Parameter estimation in a thermoelastic composite problem via adjoint formulation and model reduction (Q6557547) (← links)
- Model reduction from partial observations (Q6565196) (← links)
- A nonparametric probabilistic approach for quantifying uncertainties in low-dimensional and high-dimensional nonlinear models (Q6565216) (← links)
- Adaptive operator learning for infinite-dimensional Bayesian inverse problems (Q6669407) (← links)
