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Published on: September 7, 2019
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Uncertainty quantification for constitutive model calibration of brain tissue
Patrick T Brewick1, Kirubel Teferra1
1US Naval Research Laboratory, 4555 Overlook Ave. SW, Washington, DC 20375, USA.
Summary
This study compares calibration methods for brain tissue models, finding that more complex models improve accuracy but increase parameter uncertainty. Data characteristics significantly influence calibration outcomes.
Area of Science:
- Biomechanics
- Materials Science
- Computational Modeling
Background:
- Hyperelastic behavior of brain tissue is crucial for understanding its mechanical response.
- Ogden's constitutive model is widely used to describe this behavior.
- Accurate calibration of model parameters is essential for reliable predictions.
Purpose of the Study:
- To compare model calibration techniques for Ogden's constitutive model applied to brain tissue.
- To evaluate the impact of model complexity and experimental data characteristics on calibration.
- To ensure physically meaningful results by enforcing stability criteria.
Main Methods:
- Fitting one- and two-term Ogden models to stress-strain data using least squares and Bayesian estimation.
- Employing Hamiltonian Monte Carlo (HMC) sampling for Bayesian estimation.
- Enriching HMC to enforce the Drucker stability criterion.
- Utilizing nested sampling to determine confidence bounds and propagate them.
Main Results:
- Increased model complexity (more terms) enhances parameter accuracy but widens confidence bounds, indicating higher uncertainty.
- Calibration results are sensitive to the characteristics of the experimental data, including reported uncertainty.
- Combining disparate experimental data sets significantly affects parameter calibration.
Conclusions:
- The choice of calibration technique and data quality critically impacts the reliability of Ogden model parameters for brain tissue.
- Enforcing physical constraints like the Drucker stability criterion is vital for robust constitutive modeling.
- Future work should consider data heterogeneity and uncertainty quantification in model calibration.
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