Deep learning is rapidly emerging as a powerful tool for surrogate modeling and control in Computational Fluid Dynamics (CFD). Conceptually, these data-driven approaches are reshaping scientific practice, reviving the longstanding debate between data-driven and equation-based modeling. A central question is whether, and how, such methods can challenge or complement traditional CFD, which relies on well-established mathematical formulations. In this seminar, I will first outline the key principles that underpin efficient and reliable scientific modeling. I will then discuss how modern AI techniques can be designed to incorporate these principles and achieve practical effectiveness. In particular, I will highlight the role of implicit neural representations (INR) and uncertainty quantification (UQ) as critical components for robust and accurate surrogate modeling in aerospace engineering.