FROM DATA TO DYNAMICS: EXPLORING PHYSICS INFORMED NEURAL NETWORK SOLUTIONS FOR COMPLEX TRANSPORT PHENOMENA

Physics-Informed Neural Networks (PINNs) represent a powerful deep learning paradigm that seamlessly integrates domain-specific knowledge into machine learning models, enabling an accurate and efficient solution of complex physics-based problems. This research builds upon the foundations laid by [1], showcasing the potential of PINNs in fluid dynamics, and aligns with the growing discourse on the intersection of machine learning and physics, as exemplified by [2]. This study explores the application of PINNs in the context of solving complex nonlinear partial differential equations (PDEs) within the realm of fluid dynamics.