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Since the appearance of methods based on machine learning, they have been presented as an alternative to classical phenomenological modeling and there are few initiatives that attempt to integrate them. This paper presents a hybrid paradigm called gray box that blends a phenomenological description (differential equation) and a Support Vector Machine (SVM) to model a relevant problem in the field of cerebral hemodynamic. The results show that with this type of paradigm it is possible to exceed the results obtained with phenomenological models and also with the models based on learning, in addition to contributing to the description of the modelled phenomenon.

Gray Box Model with an SVM to Represent the Influence of PaCO2 on the Cerebral Blood Flow Autoregulation