Power transformers are pivotal assets in power systems, and accurate prediction of their remaining useful life (RUL) is essential for ensuring safe and stable grid operation. Conventional approaches, including physics based Arrhenius models and data driven machine learning methods, each face inherent limitations. To overcome these chal-lenges, this paper proposes an enhanced physics-informed neural network for dry-type transformer RUL prediction, referred to as WD-PINN (weak-form and dynamically