Conditional score-based diffusion models for solving inverse elasticity problems

Cites Work

Title not available (Why is that?)
Title not available (Why is that?)
Automated solution of differential equations by the finite element method. The FEniCS book
Circumventing the solution of inverse problems in mechanics through deep learning: application to elasticity imaging
Solution of physics-based Bayesian inverse problems with deep generative priors
Solving inverse problems using conditional invertible neural networks
A physics-informed diffusion model for high-fidelity flow field reconstruction
Uniqueness of the elastography inverse problem for incompressible nonlinear planar hyperelasticity
Voronoi Diagram in the Laguerre Geometry and Its Applications
Optimal Scaling of Discrete Approximations to Langevin Diffusions
Elastic modulus imaging: on the uniqueness and nonuniqueness of the elastography inverse problem in two dimensions
The frontier of simulation-based inference
A Connection Between Score Matching and Denoising Autoencoders
Equation of State Calculations by Fast Computing Machines
Monte Carlo sampling methods using Markov chains and their applications
Deep Generative Models and Inverse Problems
Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties
Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty
A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows
Conditional sampling with monotone GANs: from generative models to likelihood-free inference

This page was built for publication: Conditional score-based diffusion models for solving inverse elasticity problems

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6663241)