Abstract
Understanding how electromagnetic (EM) fields interact with biological tissues is crucial for various applications in healthcare and technology, such as medical imaging and wireless communication systems. Traditionally, experimental and computational methods have been used to study human exposure limits, but these approaches have limitations. More recently, machine learning (ML) methods have been used as a promising venue to address the computational challenges. In this talk, we present how artificial neural networks (ANNs) and Gaussian process regression (GPR) can be applied to predict the specific absorption rates (SAR) in human head models under uncertainties in the tissues’ electrical properties. The optimization of the networks and uncertainty estimation of the predictions is carried out.
Talk in the series “Train Your Engineering Network”.
