Artificial intelligence is changing our world as we know it. A storm of change concerns every field, everything, and everyone. In this little thought experiment, I would like to think about how our beloved Computational Fluid Dynamics (CFD) and CAE simulations are likely to change. I know, I know. Predicting the future is generally a foolish exercise, …, so I decided to write the following lines about it. Disclaimer: It’s March 2026. 

At the moment, AI is entering CFD through at least three distinct paths, each affecting engineering work in a very different way: Agents, Surrogates, and Coding. These three guys are already happening, and they are quickly changing CAE, and the pace is only growing. Let’s take a look at them one by one.

Under development ...

TBRAIN is a new software module for TCAE, an intelligent autonomous agent that automates engineering simulation workflows (especially CFD).

Its goal is to eliminate the need for expert knowledge, drastically accelerate projects, and make simulation accessible through natural-language control, similar to ChatGPT.

Hi everyone!

I want to call a tensorflow neural network while running TCAE redSolver.

Is there any relevant tutorial available?

Thank you!

The combination of fluid dynamics and machine learning has become an area of increasing interest in recent years, as researchers look for ways to use machine learning techniques to improve our understanding of fluid dynamics and make better predictions about fluid behavior.

One way that machine learning can be applied to fluid dynamics is through the use of data-driven models. These models are trained using large datasets of fluid flow data and can be used to make predictions about future fluid behavior. For example, a data-driven model could be used to predict the flow of air around an airplane wing or the movement of water in a river.

Another area where machine learning can be applied to fluid dynamics is in the optimization of fluid systems. By using machine learning algorithms to analyze fluid flow data, researchers can identify areas where fluid flow can be improved and develop optimized designs for fluid systems.

Overall, the combination of fluid dynamics and machine learning has the potential to revolutionize many scientific and engineering fields, providing new insights into fluid behavior and enabling the development of more efficient and effective fluid systems.

A forum category to discuss and exchange information, ideas, and knowledge related to the context of ML/AI applied in engineering simulations.

The goal of this category is to serve as a community of users, developers, and experts who interact and collaborate on various aspects of the software, such as usage, development, bug fixing, and feature requests. Joining this discussion group is a great way to learn from others and stay updated on the latest developments in data analysis, and ML/AI in engineering simulations.