Uncertainty Quantification of Biodegradation Models using Surrogate Models

Magnesium and its alloys are being investigated increasingly as temporary bone implant materials due to their non-toxicity, biocompatibility, and biodegradability properties. The most challenging aspect of Mg-based implants involves adapting the degradation rate to the human body, which requires extensive in vitro and in vivo testing. Reliable computational models can aid by simulating the degradation and predict its rates. Initially, a comprehensive and in-depth review of the developed degradation models up to date, revealed the fact that developing reliable degradation models is challenging. This is due to the complexity and multi-scale nature of the biodegradation process.  Furthermore, biodegradation models are inevitably characterized by uncertainty associated with different aspects of them, i.e. uncertain validation data, parameters, hypothesis and concept as well as simulator limitations. Therefore, it is critical to quantify the uncertainties within these models. Uncertainty quantification (UQ) provides several methods to quantify different sources of uncertainty within computational models. Overall, UQ methods are computationally expensive and require a substantial number of model iterations. Thus, surrogate modelling has gained significant interest in recent years, as these models can substitute expensive models within the UQ analysis. Overall, the current cumulative dissertation presents a clear workflow to quantify uncertainty within different types of biodegradation models. The published work and proposed workflow can serve as a foundation for assessing the influence of uncertainty on the reliability and robustness of the computational models of biodegradation while optimizing the ratio of accuracy and computational cost.

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Albaraghtheh, T.A.S., 2024. Uncertainty Quantification of Biodegradation Models using Surrogate Models.
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