Power electronic converters play a critical role in the conversion and control of electrical energy and constitute a fundamental building block of future energy systems. These converters rely on a broad range of semiconductor devices and passive components, such as capacitors and inductors, operating under high-frequency switching conditions. The stringent demands for high energy efficiency, elevated power density, and cost-effectiveness subject power converters to accelerated aging, increased operational stress, and a heightened risk of switching faults and system failures. Given their strategic importance in next-generation energy systems, the transition from conventional passive maintenance strategies to robust, real-time condition monitoring frameworks has become inevitable. This lecture explores how modern digital twin technologies can be integrated with traditional model-based methods to enhance monitoring capabilities. While black-box machine learning algorithms are capable of approximating the nonlinear dynamics of converters, classical identification approaches remain essential for ensuring reliable monitoring of critical system components. The lecture focuses on two advanced algorithms that combine the strengths of both paradigms: Neural Ordinary Differential Equations (NODEs) and Physics-Informed Neural Networks (PINNs). NODEs accurately approximate system dynamics by learning the underlying differential equations governing the system behavior. In contrast, PINNs embed physical laws directly into the learning process by enforcing consistency between the converter’s input–output data and its governing equations, including parameter dependencies. By maintaining physical consistency during training and leveraging automatic differentiation, these architectures enable the estimation of unmeasured states and the detection of parameter degradation, while significantly reducing the amount of training data required. The lecture introduces the theoretical foundations of NODEs and PINNs and presents a unified architecture illustrated through a DC–DC buck converter simulation example.