Ai stands for Artificial Intelligence.

HPCSQUARE addresses the space in Machine Learning ranging from
statistical tools, like low data requirement ensemble models(e.g. random forest), support vector machines, naive Bayes, etc, to unsupervised learning techniques like k-means clustering, and also state-of-the-art deep networks.
In contrast to ML algorithms that may require manual features engineering to improve performance, deep networks learn from data (=supervised training) and require less manual work. Some examples are (non exhaustive list): CNNs, YOLO for object detection, Autoencoders, RESNET, Inception networks, GANs, LSTM, Transformers.
We can also address a range of performance options and software engineering options offered by Nvidia (compiler, profiler, NGC,…) and by Microsoft Azure in the area of auto-ml and as-a-service AI.

In context of HPC, ML can be used to significantly reduce the time-to-solution of CFD applications: