Regularized Domain Adaptation for Estimation Tasks in Partially Observed Target Domains

The performance of machine learning algorithms is limited by the availability of training data. Transfer learning can alleviate this limitation by adapting models trained in data-rich domains to a data-sparse domain. In this work, we propose a method for data augmentation in a partially sampled datasparse target domain by transporting structural insights on data from a data-rich source domain. As an example, we show how this approach can improve the performance of a battery core temperature estimation based on electrochemical impedance spectroscopy (EIS) measurements, when only limited training data is available for new battery chemistries and form factors.