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Best Practices in Developing a Workflow for Uncertainty Quantification for Modeling the Biodegradation of Mg-Based
Tamadur AlBaraghtheh1,2, Regine Willumeit-Römer1, Berit Zeller-Plumhoff1,3
1Institute of Metallic Biomaterials, Helmholtz-Zentrum hereon GmbH, Max-Planck-Straße 1, 21502, Geesthacht, Germany.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|October 25, 2024
Summary
Quantifying uncertainty in magnesium implant biodegradation models is crucial. This study introduces a workflow using surrogate models, finding Kriging efficient for calibration and polynomial chaos methods better for predicting uncertainties.
Area of Science:
- Biomaterials Science
- Computational Modeling
- Materials Engineering
Background:
- Computational models for magnesium (Mg)-based implant biodegradation are essential but face significant uncertainty.
- Iterative evaluations are needed to quantify this uncertainty, posing a challenge for complex, multiscale models.
- Surrogate models offer a computationally efficient alternative to high-cost, complex models, but their application to biodegradation is understudied.
Purpose of the Study:
- To develop and evaluate a workflow for quantifying uncertainties in Mg-based implant biodegradation models.
- To compare the performance of three surrogate models: Kriging, polynomial chaos expansion (PCE), and polynomial chaos Kriging (PCK).
- To assess the suitability of these surrogate models for global sensitivity analysis and uncertainty propagation.
Main Methods:
- Implementation of a workflow for uncertainty quantification in biodegradation models.
- Construction and evaluation of Kriging, PCE, and PCK surrogate models.
- Testing surrogate models against three computational models of Mg-based implant biodegradation.
- Application of global sensitivity analysis and uncertainty propagation.
Main Results:
- Kriging demonstrated effectiveness in calibrating computational models with reduced computational time and cost.
- Polynomial chaos expansion and polynomial chaos Kriging showed superior capability in predicting propagated uncertainties.
- The study successfully quantified different types of uncertainty within the biodegradation models.
Conclusions:
- The developed workflow provides a robust method for uncertainty quantification in Mg-based implant biodegradation.
- Kriging is recommended for model calibration due to its efficiency, while PCE and PCK are better suited for uncertainty propagation analysis.
- This research addresses the understudied application of surrogate modeling for complex degradation processes in biomedical implants.
