Adjoint Hamiltonian Monte Carlo algorithm for the estimation of elastic modulus through the inversion of elastic wave propagation data

Markov processes (60Jxx) Partial differential equations of mathematical physics and other areas of application (35Qxx) Probabilistic methods, stochastic differential equations (65Cxx)

Cites Work

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Robust adaptive Metropolis algorithm with coerced acceptance rate
Primal-dual subgradient methods for convex problems
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
Topology optimization for transient wave propagation problems in one dimension
Identification of uncertainties in the shape of geophysical objects with level sets and the adjoint method
General state space Markov chains and MCMC algorithms
Dimensionality reduction and polynomial chaos acceleration of Bayesian inference in inverse problems
An introduction to MCMC for machine learning
Optimal scaling for various Metropolis-Hastings algorithms.
Fast derivatives of likelihood functionals for ODE based models using adjoint-state method
Inference from iterative simulation using multiple sequences
Bayesian learning for neural networks
Parameter estimation in stochastic differential equations with Markov chain Monte Carlo and non-linear Kalman filtering
Coordinate transformation and polynomial chaos for the Bayesian inference of a Gaussian process with parametrized prior covariance function
A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion
Solving large-scale PDE-constrained Bayesian inverse problems with Riemann manifold Hamiltonian Monte Carlo
A Time Integration Algorithm for Structural Dynamics With Improved Numerical Dissipation: The Generalized-α Method
The adjoint method in seismology
An alpha modification of Newmark's method
Inverse Problem Theory and Methods for Model Parameter Estimation
Equation of State Calculations by Fast Computing Machines
A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems Part I: The Linearized Case, with Application to Global Seismic Inversion
Preconditioning Markov Chain Monte Carlo Simulations Using Coarse-Scale Models
A Stochastic Approximation Method
An adaptive Metropolis algorithm

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