Efficient Bayesian physics informed neural networks for inverse problems via ensemble Kalman inversion

Cites Work

Unnamed Item
Iterative regularization for ensemble data assimilation in reservoir models
Spectral and finite difference solutions of the Burgers equations
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN
Challenges in Markov chain Monte Carlo for Bayesian neural networks
Stein variational gradient descent with local approximations
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Ensemble Kalman inversion for general likelihoods
Iterated Kalman methodology for inverse problems
Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons
A regularizing iterative ensemble Kalman method for PDE-constrained inverse problems
Ensemble Kalman methods for inverse problems
Regularizing properties of a truncated newton-cg algorithm for nonlinear inverse problems
Parameterizations for ensemble Kalman inversion
Ensemble Kalman method for learning turbulence models from indirect observation data
Efficient derivative-free Bayesian inference for large-scale inverse problems
Convergence acceleration of ensemble Kalman inversion in nonlinear settings
14 Ensemble Kalman filter for neural network-based one-shot inversion
Tikhonov Regularization within Ensemble Kalman Inversion
DeepXDE: A Deep Learning Library for Solving Differential Equations
Ensemble Kalman inversion: a derivative-free technique for machine learning tasks
Localized ensemble Kalman inversion
Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance

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