Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems
From MaRDI portal
Publication:4582677
DOI10.1088/1361-6420/aad210zbMath1475.65037arXiv1802.06517OpenAlexW2788406418MaRDI QIDQ4582677
No author found.
Publication date: 24 August 2018
Published in: Inverse Problems (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1802.06517
Bayesian inference (62F15) Ill-posedness and regularization problems in numerical linear algebra (65F22) Numerical mathematical programming methods (65K05) Nonlinear programming (90C30)
Related Items
An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement ⋮ Learning physics-based models from data: perspectives from inverse problems and model reduction ⋮ Stochastic Learning Approach for Binary Optimization: Application to Bayesian Optimal Design of Experiments ⋮ Optimal Experimental Design for Inverse Problems in the Presence of Observation Correlations ⋮ Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors ⋮ A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design ⋮ A greedy sensor selection algorithm for hyperparameterized linear Bayesian inverse problems with correlated noise models ⋮ Large-scale Bayesian optimal experimental design with derivative-informed projected neural network ⋮ Optimal experimental design under irreducible uncertainty for linear inverse problems governed by PDEs ⋮ Randomization and Reweighted $\ell_1$-Minimization for A-Optimal Design of Linear Inverse Problems ⋮ Optimal Design of Large-scale Bayesian Linear Inverse Problems Under Reducible Model Uncertainty: Good to Know What You Don't Know ⋮ Sequentially optimized projections in x-ray imaging * ⋮ Optimal experimental design for prediction based on push-forward probability measures ⋮ Optimal experimental design for infinite-dimensional Bayesian inverse problems governed by PDEs: a review ⋮ Edge-Promoting Adaptive Bayesian Experimental Design for X-ray Imaging
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- On Bayesian A- and D-optimal experimental designs in infinite dimensions
- Numerical methods for \(A\)-optimal designs with a sparsity constraint for ill-posed inverse problems
- Automated solution of differential equations by the finite element method. The FEniCS book
- Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the antarctic ice sheet
- Expected information as ecpected utility
- Numerical methods for optimum experimental design in DAE systems
- Bayesian experimental design: A review
- Fast Bayesian optimal experimental design for seismic source inversion
- A Laplace method for under-determined Bayesian optimal experimental designs
- A note on Bayesian \(c\)-and \(D\)-optimal designs in nonlinear regression models
- Fast Bayesian experimental design: Laplace-based importance sampling for the expected information gain
- Randomized matrix-free trace and log-determinant estimators
- Fast estimation of expected information gains for Bayesian experimental designs based on Laplace approximations
- An Algorithm for Construction of Constrained D-Optimum Designs
- Nonlinear Goal-Oriented Bayesian Inference: Application to Carbon Capture and Storage
- Experimental Design for Biological Systems
- A-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with Regularized $\ell_0$-Sparsification
- Remark on “algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization”
- Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix
- Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian Approximations
- On a Measure of the Information Provided by an Experiment
- Optimum Designs in Regression Problems
- A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems
- Numerical methods for experimental design of large-scale linear ill-posed inverse problems
- Guided Bayesian optimal experimental design
- Optimal Measurement Methods for Distributed Parameter System Identification
- A Technique for the Numerical Solution of Certain Integral Equations of the First Kind
- Algorithm 778: L-BFGS-B
- Principles of Optimal Design
- Numerical methods for optimal control problems in design of robust optimal experiments for nonlinear dynamic processes
- Inverse Problem Theory and Methods for Model Parameter Estimation
- Efficient D-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems
- A Limited Memory Algorithm for Bound Constrained Optimization
- Experimental design in the context of Tikhonov regularized inverse problems
- GRADIENT-BASED STOCHASTIC OPTIMIZATION METHODS IN BAYESIAN EXPERIMENTAL DESIGN
- Numerical methods for the design of large-scale nonlinear discrete ill-posed inverse problems
- A-optimal encoding weights for nonlinear inverse problems, with application to the Helmholtz inverse problem
- Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems
- A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems Part I: The Linearized Case, with Application to Global Seismic Inversion
- On Information and Sufficiency
- Optimum Allocation in Linear Regression Theory
- Goal-Oriented Inference: Approach, Linear Theory, and Application to Advection Diffusion
