Physics-Informed Neural Networks (PINNs) are a powerful and emerging tool for solving problems related to dynamical systems governed by Ordinary Differential Equations (ODEs). Unlike traditional data-driven models, PINNs incorporate the residuals of differential operators directly into the cost function, ensuring that the learned solution is not only data-consistent but also physically plausible. This enables the network to approximate system dynamics while minimizing the discrepancy between observed and theoretically expected behaviors. This study focuses on the development of a PINN that employs the inverse problem approach to estimate the passive parameters of an AC/DC power converter, specifically its inductances and capacitance. These parameters play a crucial role in the converter’s operation: inductances influence the dynamic response of the system by regulating current variations, while the capacitance C stabilizes the DC-link voltage, ensuring proper energy storage and distribution.