Calibrate, emulate, sample
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Publication:2123875
DOI10.1016/j.jcp.2020.109716OpenAlexW3040929669MaRDI QIDQ2123875
Emmet Cleary, Andrew M. Stuart, Tapio Schneider, Alfredo Garbuno-Inigo, Shiwei Lan
Publication date: 14 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2001.03689
uncertainty quantificationexperimental designGaussian process emulationapproximate Bayesian inversionensemble Kalman sampling
Related Items (10)
Adaptive Tikhonov strategies for stochastic ensemble Kalman inversion ⋮ Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods ⋮ Iterated Kalman methodology for inverse problems ⋮ Localized ensemble Kalman inversion ⋮ Consensus‐based sampling ⋮ Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement ⋮ Bayesian spatiotemporal modeling for inverse problems ⋮ A surrogate-based approach to nonlinear, non-Gaussian joint state-parameter data assimilation ⋮ Kernel-based parameter estimation of dynamical systems with unknown observation functions ⋮ Ensemble Kalman inversion for sparse learning of dynamical systems from time-averaged data
Uses Software
Cites Work
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- Multi-output separable Gaussian process: towards an efficient, fully Bayesian paradigm for uncertainty quantification
- Predicting the future. Completing models of observed complex systems
- Emulation of higher-order tensors in manifold Monte Carlo methods for Bayesian inverse problems
- General state space Markov chains and MCMC algorithms
- Exponential convergence of Langevin distributions and their discrete approximations
- Evaluation of Gaussian approximations for data assimilation in reservoir models
- Ensemble preconditioning for Markov chain Monte Carlo simulation
- Investigation of the sampling performance of ensemble-based methods with a simple reservoir model
- Analysis of iterative ensemble smoothers for solving inverse problems
- The effect of the nugget on Gaussian process emulators of computer models
- Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems
- Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion
- Bayesian Calibration of Computer Models
- Ensemble Kalman methods for inverse problems
- Adaptive Construction of Surrogates for the Bayesian Solution of Inverse Problems
- Kernels for Vector-Valued Functions: A Review
- Sequential Monte Carlo Methods for High-Dimensional Inverse Problems: A Case Study for the Navier--Stokes Equations
- Sequential Monte Carlo Samplers
- Analysis of the Ensemble and Polynomial Chaos Kalman Filters in Bayesian Inverse Problems
- A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems
- Computer Model Calibration Using High-Dimensional Output
- Optimal Scaling of Discrete Approximations to Langevin Diffusions
- Mixed Finite Elements for Elliptic Problems with Tensor Coefficients as Cell-Centered Finite Differences
- Posterior consistency for Gaussian process approximations of Bayesian posterior distributions
- Deterministic Nonperiodic Flow
- Convergence analysis of ensemble Kalman inversion: the linear, noisy case
- Combining Field Data and Computer Simulations for Calibration and Prediction
- Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach
- AN ADAPTIVE MULTIFIDELITY PC-BASED ENSEMBLE KALMAN INVERSION FOR INVERSE PROBLEMS
- Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler
- An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems
- Stochastic Processes and Applications
- Ensemble Kalman inversion: a derivative-free technique for machine learning tasks
- Ensemble Kalman methods with constraints
- Multilevel Adaptive Sparse Leja Approximations for Bayesian Inverse Problems
- Equation of State Calculations by Fast Computing Machines
- Data Assimilation
- Analysis of the Ensemble Kalman Filter for Inverse Problems
- Chapter 1: Introduction to data assimilation and inverse problems
- Filtering Complex Turbulent Systems
- Monte Carlo sampling methods using Markov chains and their applications
- The Monte Carlo Method
- MCMC methods for functions: modifying old algorithms to make them faster
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