## 2 AI in CFD: Overview and Perspective
### Using AI in CFD  
With the advancement of the 20 th century and the increasing use of artificial intelligence (AI), researchers began to implement this new technology in CFD for various purposes. Among the most common uses of AI in CFD are the acceleration of calculations, whether equations or algorithms, through neural networks that replace or complement parts of the traditional process; the generation of CFD simulations via AI; or the creation of hybrid models that combine CFD and AI for different aspects [14; 15; 16; 17; 18].  
Although it is a relatively recent field of research, many of the most common AI techniques were applied for its use in CFD. There are researchers leveraging the capabilities of genetic algorithms to produce optimal or quasi-optimal designs as in works by [PERSON] et al. [19] and [PERSON] et al. [20], who combine genetic algorithms with neural networks and apply that hybrid approach to optimization in aerodynamic designs and micro photoreceptor designs, respectively. Both studies concluded that, for their specific areas, these new implementations achieved optimal results compared to conventional CFD tools, improving efficiency and result accuracy [19; 20].  
Artificial neural networks (ANNs) are one of the most commonly used AI techniques to complement or substitute CFD. The nature of most CFD problems, represented as bidimensional data point matrices evolving along the time axis, kind of image-like or video-like, suits perfectly with most of the deep neural network (DNN) approaches, such as convolutional neural networks (CNNs), autoencoders (AEs), and recurrent neural networks (RNNs).  
For example, the study by [PERSON] et al. [21] proposed the use of spectral convolutional neural networks to optimize the solution of partial differential equations (used in CFD); the results indicate that the predictions were accurate in transient flow patterns around submerged bodies, although with limitations that subsequent studies aim to improve. Studies by [PERSON] et al. [22] and [PERSON] et al. [23] propose using artificial neural networks [24] and convolutional neural networks [25], respectively, as alternatives to conventional CFD methods. In both studies, CFD simulations were used to train and validate the neural networks, achieving results and demonstrating that neural networks were an optimal and accurate alternative that can reduce computational costs. One of the most promising neural network-based approaches is the use of physically informed neural networks (PINNs); they are neural networks constrained during the learning (building) phase by the governing equations behind the physical model they are representing. That approach combines the generalization power of neural networks with the precision of the physical model equations. Works like [26; 27; 28] use PINN to approach the solutions of CFD problems by using those neural network models as a surrogate model of the Navier-Stokes equations governing CFD. Although neural network models require high volumes of data and time to be trained (built), once that phase is completed, they can infer (solve) CFD problems in a fraction of the time it takes a CFD solver. (Most neural networks have inference times that are nearly instantaneous).