Abstract
Traumatic brain injury (TBI) is modeled using in vitro mechanical testing on excised brain tissue samples. While such testing is essential for understanding the mechanics of TBI, the results can vary by orders of magnitude due to the varying testing condition protocols. Gaussian process regression (GPR) provides good predictive accuracy of the compressive stress state. Here, the efficacy of different search algorithms in optimizing GPR hyperparameters was evaluated. Bayesian optimization, grid search, and random search were compared. Grid search reached the minimum objective function in fewer iterations, and the final regression model was comparable to that of Bayesian optimization in terms of RMSE and log likelihood in the prediction of compressive stress.
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Acknowledgment
The authors are grateful to the Center for Advanced Vehicular Systems, Mississippi State University for their support.
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Patterson, F., Abuomar, O., Prabhu, R.K. (2021). Hyperparameter Optimization Algorithms for Gaussian Process Regression of Brain Tissue Compressive Stress. In: Stahlbock, R., Weiss, G.M., Abou-Nasr, M., Yang, CY., Arabnia, H.R., Deligiannidis, L. (eds) Advances in Data Science and Information Engineering. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-71704-9_41
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DOI: https://doi.org/10.1007/978-3-030-71704-9_41
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