Parameter inference based on Gaussian processes informed by nonlinear partial differential equations

Cites Work

Unnamed Item
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Bayesian solution uncertainty quantification for differential equations
Emulation of higher-order tensors in manifold Monte Carlo methods for Bayesian inverse problems
Stochastic spectral methods for efficient Bayesian solution of inverse problems
Efficient global optimization of expensive black-box functions
Solving partial differential equations by collocation using radial basis functions
Fitting parameters in partial differential equations from partially observed noisy data
Machine learning of linear differential equations using Gaussian processes
The diffusion-Brusselator equation
Solving and learning nonlinear PDEs with Gaussian processes
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Inferring the unknown parameters in differential equation by Gaussian process regression with constraint
Bayesian Calibration of Computer Models
Parameter Estimation of Partial Differential Equation Models
Adaptive Construction of Surrogates for the Bayesian Solution of Inverse Problems
Inverse problems: A Bayesian perspective
A new numerical method for the inverse source problem from a Bayesian perspective
Experiments
Handbook of Markov Chain Monte Carlo
Posterior consistency for Gaussian process approximations of Bayesian posterior distributions
Gaussian Process Modeling with Boundary Information
Linear and Nonlinear Inverse Problems with Practical Applications
PARAMETER IDENTIFICATION TECHNIQUES FOR PARTIAL DIFFERENTIAL EQUATIONS
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations
Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment
Mathematical Models in Epidemiology
Computational Study of Fast Methods for the Eikonal Equation
Modularization in Bayesian analysis, with emphasis on analysis of computer models
A Gaussian Process Emulator Based Approach for Bayesian Calibration of a Functional Input
Gaussian Process Assisted Active Learning of Physical Laws

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