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Related Experiment Video

Updated: Dec 6, 2025

A Method for Studying the Temperature Dependence of Dynamic Fracture and Fragmentation

A Method for Studying the Temperature Dependence of Dynamic Fracture and Fragmentation

Published on: June 28, 2015

A Bayesian estimation method for variational phase-field fracture problems.

Amirreza Khodadadian^1,2, Nima Noii², Maryam Parvizi¹

¹Institute of Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8-10, 1040 Vienna, Austria.

Computational Mechanics

|October 8, 2020

Summary

This summary is machine-generated.

This study introduces a Bayesian framework for estimating parameters in fracture propagation problems using phase-field modeling. The method efficiently handles computational costs by fitting parameters on coarser meshes, accurately matching reference load-displacement curves.

Keywords:

Bayesian estimation Brittle fracture Inverse problem Multi-field problem Phase-field propagation

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Area of Science:

Computational mechanics
Solid mechanics
Materials science

Background:

Fracture propagation is critical in engineering, but parameter estimation is computationally intensive.
Phase-field methods offer a robust way to model complex fracture behaviors.
Uncertainties in material parameters and fracture toughness significantly impact model predictions.

Purpose of the Study:

To develop an efficient Bayesian parameter estimation framework for fracture propagation problems.
To address uncertainties in solid material parameters and critical energy release rate.
To reduce the high computational costs associated with time- and mesh-dependent simulations.

Main Methods:

Utilizing a phase-field method to describe fracture propagation.
Employing a Bayesian approach for parameter estimation.
Implementing Bayesian inversion on a coarse mesh to fit parameters efficiently.

Main Results:

The proposed framework successfully estimates parameters for fracture propagation.
Load-displacement curves generated by the framework closely match reference values.
Computational efficiency is improved by performing inversion on a coarser mesh.

Conclusions:

The Bayesian parameter estimation framework provides a computationally efficient and accurate solution for phase-field fracture models.
The approach effectively quantifies uncertainties in material parameters and fracture toughness.
This method substantiates the use of Bayesian inversion for complex solid mechanics problems.