Concluding remarks

The merging of artificial intelligence (AI) and computational fluid dynamics (CFD) has transformed the domain with accelerated simulation processes, improved turbulence modeling, and diversified scope into real-life applications. This chapter summarizes everything already covered in the book and discusses AI’s effect on both classical CFD approaches, with a focus on advancements such as AI surrogate models, physics-based neural networks (PINNs), and data-driven turbulence models. Simulations with an optimized AI will enhance our understanding of the complex behavior of fluids, which is particularly relevant for industries such as aerospace, automotive, and environmental engineering, giving rise to efficient designs and fast simulations. AI has been very helpful, but there are still several stumbling blocks to be overcome: a concern about relevant data availability, model generalization, and computational cost. Robust AI models are necessary to generalize to conditions never encountered in the training set and couple nicely with existing CFD workflows. Despite the hurdles existing today, such perspectives for the AI-CFD in terms of future research, such as hybrid models, quantum computing, and self-supervised learning, all hold great potential in revamping the at-hand limitations. Ultimately, the chapter closes AI’s role in CFD, considering it not a mere technological advancement but a paradigm shift in fluid dynamics modeling. Consequently, these evolving AI techniques mixed with classical CFD methods shall endorse the arrival of faster, accurate, and reliable simulations, thus engendering a plethora of innovations, spanning from research to business applications. Thereby laying out a pathway for AI-CFD to become a valuable tool for solving fluid dynamic problems in many disciplines.