Simulation-informed kernel-based regression models for predicting temperature distribution in microwave hyperthermia.

This work analyzes heat transfer in microwave heating process using hyperthermic approach. Predictive performance of artificial intelligence (AI) models coupled with data of mechanistic model were evaluated. The resulting predictive models provide a surrogate representation of the simulated temperature field and may support temperature assessment in microwave hyperthermia applications. The model is developed in two stages, where in the first stage, electromagnetic heating is modeled via solution of bioheat equations to compute temperature field in the tissue. Then, the results are used for building several artificial intelligence-based models. The predictive stage of the study was carried out using three nonlinear kernel-driven learning approaches, namely Support Vector Machine (SVM), Kernel Ridge Regression (KRR), and Relevance Vector Machine (RVM), with the spatial coordinates serving as predictors of the temperature field. Model-specific hyperparameters were determined through a Cuckoo Search-based optimization procedure. Among the evaluated algorithms, SVM delivered the strongest overall predictive performance, yielding R