Recent advances in AI, driven by increased computational power, are expanding capabilities in aerospace. While machine learning (ML) reveals hidden data patterns, it typically requires large datasets. Numerical simulations offer insights but are costly and sometimes unreliable due to model limitations. Experimental testing, though essential, is expensive and yields limited data. Physics-Informed Neural Networks (PINNs) integrate physical laws into ML models, reducing data needs and improving accuracy. This study presents a PINN tool developed in Python with PyTorch, supporting flexible architectures and adaptable cost functions. PINNs were applied to aerospace use cases, including temperature prediction, computational fluid dynamics (CFD), and Solid Rocket Motor modelling. Compared to traditional numerical and purely data-driven methods, PINNs demonstrated improved accuracy and data efficiency, though with higher computational costs. They also show promise as surrogate models. Future work will focus on optimising for dedicated hardware, deeper architectures, and broader applications to further explore their potential in aerospace simulation and modelling.