Part 1: AI/ML in Solvers (CFD, FEA)

Lots of attention has always been given to ML models trained on historical datasets -- but here we talk about integration AI/ML machinery into our CFD/FEA numerical solvers. Part 1 = CFD focus.

Let’s make a survey of use cases for how AI/ML is being used in our CFD and FEA numerical frameworks. All the time we see publications for new AI/ML models that emerge, but we want to go beyond scenarios where we train models on bodies of pre-prepared simulation data that are used for making predictions thereafter as a surrogate to additional simulation. With the ever-increasing need for higher fidelity simulation in more accessible manners, as you can get a sense from the image below [1] (figure 1 from the “CFD vision 2030“ roadmap), we also want to consider how AI/ML can assist the simulations themselves when running (and not just ‘replace’ solvers by training on historical datasets).

Stated casually — the future will continually require more complicated and expensive simulation, so let’s how AI/ML can help make those simulations faster/cheaper/more accessible.