In this work, we present a new method that incorporates physical information to enhance prediction accuracy without requiring the solution of complex partial differential equations. To validate our approach, we generated a dataset through simulations in SolidWorks and compared our predictions with the simulated values. Our experiments demonstrate that the proposed method outperforms two different neural network-based baseline models. This framework enables faster and more accurate predictions, making it an essential tool for applications where both speed and precision are critical. Furthermore, our approach simplifies the modeling process by removing the reliance on complex numerical computations, providing a more efficient and accessible solution for real-world applications.