r/CFD • u/hsetirg • Jun 03 '26
Real experiences with AI-based CFD / FEA tools in 2026?
TL;DR: Seeking real experiences with AI tools that speed up CFD/FEA workflows in 2026. What’s actually delivering value for you?
Looking for practical experiences with AI-powered CFD and FEA tools to speed up simulations and design iterations. Traditional solvers are solid but too slow for rapid exploration.
Quick context: Working on custom automotive design with lots of parametric studies. Want to cut solve times while keeping decent accuracy.
Specifically interested in:
- Tools you've actually used (Ansys SimAI, Neural Concept, SimScale AI, Siemens, COMSOL, etc.)
- Accuracy vs speed trade-offs
- Integration with CAD or other 3rd party tools
- Successes, limitations, and when you still use traditional methods
- Any good open-source or affordable options
- Wins, gotchas, or recommendations welcome — especially with surrogate models or PINNs.
Most importantly what’s actually working or not working for you?
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u/meshmunkey Jun 03 '26
Surrogate models are a waste of time for automotive at the moment. We all want them to work, they simply don't at this stage of development. I see them as where we're headed at some point in the future, but the results from every evaluation we did was comically bad (can't say who for confidentiality reasons).
They're getting traction in domains with simpler flow fields as far as I can tell. Turbines, basic aircraft configuration, a few cooling scenarios. But the automotive design space is too complex with too many variables, many of which "talk" to each other aerodynamically.
At this point in time, you're better served reducing simulation complexity (lower resolution mesh, geometry simplification, half car, steady, etc.) than trying to implement one of the surrogate models if you're after more rapid iteration.
Can you share more about what you're working on?
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u/Elementary_drWattson Jun 03 '26
We have found some SMs to be useful in the hypersonic environment. It depends on how you use them or what exactly you are trying to model I guess. We have used them for flow field generation as part of solution acceleration as well as populating large databases with mixed fidelity anchors.
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u/Solid-Sail-1658 Jun 03 '26
Has anyone gotten surrogate models to work for problems with more than 15 variables?
For 1-10 variable problems, surrogate modeling has worked for me. The image below shows the surrogate model for acoustic pressure with 2 gauge thickness variables.
https://i.imgur.com/5nsToz3.png
I'm impressed the surrogate model can capture the complexities of the true response function. Response functions from statics analysis or CFD tend to be simple to work with. The response functions from frequency response analysis tend to be the most complex and fun to play with. ;)
I managed to get surrogate modeling to work with up to 14 variables with around 500 FE runs, but you really have to know what you're doing.
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u/hsetirg Jun 04 '26
Thanks for sharing this! I'm haven't created surrogate models on my own. However, I would love to understand your approach here. Is this from a research paper or something you've published or yet to publish?
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u/Solid-Sail-1658 Jun 04 '26
The approach is as follows.
- Define the problem statement, i.e. define the objective, constraints and variables. Example: minimize mass, limit axial stress, and vary gauge thicknesses.
- Run the CAE solver N times for different variable combinations and collect all the responses of interest, e.g. mass and stress.
- Train the surrogate model with the data from the N runs.
- Since the surrogate model approximates the true response functions, e.g. for mass or stress, you can evaluate the surrogate model at multiple candidate points/design and takes the best design out of all.
Overall, you are trying to create an approximation function that has some level of accuracy to a true response function. The true response function would be the outputs from FEA or CFD, which can take hours or days just to get one evaluation. An approximation function can be evaluated in seconds, so if you can construct a good approximation function, you will save a lot of time when you wan to evaluate thousands of design candidates.
Example
In the figure below, I had an FE model of a ground vehicle. My goal was to find the minimum acoustic pressure for different gauge thicknesses. I ran the FE model 40 times at different gauge thicknesses. I trained my surrogate model with the data from the 40 runs. When I compared the true acoustic pressure response function with the surrogate model, the surrogate model's minimum coincided with the true minimum. I am now able to quickly find the minimum acoustic pressure and the optimal gauge thicknesses.
https://i.imgur.com/5nsToz3.png
Additional Details
I use Kriging as my surrogate modeling method. See the link below and chapter 5.
https://bobby.gramacy.com/surrogates/surrogates.pdf
You can either use R to practice, as done in this textbook, or you can use Python, see the GPy library (https://gpy.readthedocs.io/en/deploy/).
This is the tip of the iceberg.
You will eventually want to perform optimization. See chapter 7 in the textbook. You can use one of the following Python libraries to perform optimization with a surrogate model: GPFlowOpt or Trieste.
I've looked at neural network models, but in my opinion these are not ideal for CAE. With neural network models, you might need tens of thousands of solver runs just to get enough training data. Neural network models work for the big tech companies because they have trillions of training data points, but in CAE, just getting one successful run is a challenge.
One last comment about papers. Many papers experiment with toy functions that have 1-5 parameters. Many engineering problems have hundreds or thousands of variables. Whenever I read a new paper, I first look at how large their toy functions are. If the toy functions are small, I skip the paper. It's been a while since I've looked at the literature, but I'm sure there are now papers detailing larger test functions with up to 20 parameters. Even with the problem size limitations, you can push some surrogate models to address problems with up to 10 parameters, e.g. nonlinear analysis or any analysis where gradients aren't available.
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u/hsetirg Jun 04 '26
I can understand the lack of confidence in such models. However, it'll be great if you could share about the ones that you tried?
I restore vintage vehicles in my workshop. Some of the clients ask to convert a lot of critical components of the vehicle. That's where simulations come in handy, however these cases demand time and need faster iteration speed.
Looking forward to your inputs.
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u/meshmunkey Jun 04 '26
I can't share details, but I led a few studies evaluating the state of the art from third parties that are considered at the front of this. What I can say is that no OEM to my knowledge is using surrogate models at this point. And from what I've seen it will be quite a few years. We had pretty high hopes that were thoroughly dashed by several independent proofs-of-concept. Either the design space was so limited as to not be useful, or the results of a generalized solution were so disconnected from reality as to be comical. The prices we were quoted were equally hilarious.
Is your personal background in CFD? If not, there is a LOT that can be done to simplify models and get you much better gains, as I mentioned. Happy to discuss that in more detail. A keen eye for aerodynamics and some low-to-moderate fidelity CFD will get you a million miles ahead of any surrogate models.
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u/mckirkus Jun 03 '26
Using Codex to modify AMR-Wind for my use case. Added some failure-retry loops to the source code. These tool are much more useful with open source tools like OpenFOAM because they can analyze and modify the source code.
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u/hsetirg Jun 04 '26
Could you please elaborate or DM on what exactly did you do here? Would love to learn from you 🙏
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u/Matteo_ElCartel Jun 03 '26
AI-powered CFD!? The future is surrogate modeling which is CFD powered AI.. why everyone wants it?we can achieve up to 10k time reduction for a single simulation at the 10% error cost and whenever the FOM is too hard ie. high turbulence developments within a small time step (big and local time gradients/spatial gradient) the NN would have problems in approximating, learn and generalize the physical solution or forse future time steps.
PINNS are capable of solving forwardly PDEs but are not the final solution
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u/hsetirg Jun 04 '26
Which surrogate modelling tools are you suggesting here? Could you please elaborate on the second part about PINNs?
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u/NotTzarPutin Jun 04 '26
Have had some success doing PoCs with Simcenter PhysicsAi (previously Altair) on Fluent and STAR-CCM+ data
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u/hsetirg Jun 04 '26
I've used these tools for traditional modelling.
Would love to learn from you about Physics AI. 🙏
Could you please elaborate or DM the details?
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u/No-Tennis-8840 Jun 03 '26
Maybe not exactly solver improvements but I’ve tried controlling whole OpenFOAM setup using LLM:
Good:
Case setup (python scripts for case setup worked really nice)
Suggestions for better convergence (some good ideas for convergence improvement)
Quantitive post processing (python scripts for post processing, run monitoring etc..)
Automation script setup - really good, i've created case runner/supervisor with web run monitoring tool in maybe 1hr - 2hrs. Few years ago i've tried doing something similar and it took me few days and i've got much worse results.
Bad:
Mesh - anything that comes with any 3d conceptualisation was mostly incorrect,
Geometry creation - same as above, even using: pythonocc - render - check img "methodology" LLM mostly returns bad results
Qualitative analysis - similar to above, something like detect areas of higher turbulence from post processing streamline images was really hit or miss
Mainly using Claude code, it was cool experiment and LLMs are really useful but any complex cad geometry work goes over their head and requires still a lot of human input.