Adaptive neural network surrogate model for solving the nonlinear elastic inverse problem via Bayesian inference

DOI10.1515/jiip-2022-0050MaRDI QIDQ6583083

Kai Zhang, Jingzhi Li, Yu Gao, Fuchang Huo

Publication date: 6 August 2024

zbMATH Keywords

Markov chain Monte CarloBayesian inverse problemssurrogate modelingnonlinear elasticity problems

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Bayesian inference (62F15) Classical linear elasticity (74B05) Inverse problems in equilibrium solid mechanics (74G75)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Solution of the nonlinear elasticity imaging inverse problem: the incompressible case
• A neural network scheme for recovering scattering obstacles with limited phaseless far-field data
• On some mixed finite element methods for incompressible and nearly incompressible finite elasticity
• A Bayesian approach to selecting hyperelastic constitutive models of soft tissue
• Circumventing the solution of inverse problems in mechanics through deep learning: application to elasticity imaging
• A neural network method for time-dependent inverse source problem with limited-aperture data
• Randomized approaches to accelerate MCMC algorithms for Bayesian inverse problems
• On an artificial neural network for inverse scattering problems
• Stable determination of an elastic medium scatterer by a single far-field measurement and beyond
• Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems
• The finite element method with Lagrangian multipliers
• Adaptive neural network surrogate model for solving the implied volatility of time-dependent American option via Bayesian inference
• Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems
• An efficient Bayesian inference approach to inverse problems based on an adaptive sparse grid collocation method
• Posterior consistency for Gaussian process approximations of Bayesian posterior distributions
• Effective Medium Theory for Embedded Obstacles in Elasticity with Applications to Inverse Problems
• An Efficient Numerical Method for the Valuation of American Better-of Options Based on the Front-Fixing Transform and the Far Field Truncation
• An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems
• Nonlinear Continuum Mechanics for Finite Element Analysis

This page was built for publication: Adaptive neural network surrogate model for solving the nonlinear elastic inverse problem via Bayesian inference