r/fea • u/Maleficent_Play1092 • 4d ago
Is learning Python still worth it for simulation engineers now that tools like optiSLang exist?
I work as an FEA engineer. During an internship I was exposed to optiSLang (DOE, sensitivity analysis, MOP), and that got me interested in moving toward AI/ML for simulation.
If you work in this area:
- Which path did you take, and what would you do differently?
- What skills do employers or teams actually ask for?
- Any project types that worked well as a portfolio (e.g. surrogate vs. full FEA on a simple structure)?
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u/BatyStar 4d ago
I mean, just because optislang exists doesn't mean your employer will buy the licence.
Tbh, I use python mostly for nice graphs with mathplotlib, or for automating Meta anyway, not for optimization algorithms.
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u/Destiny7655 4d ago
AI/ML in simulation is really currently âhave a doctorateâ level. It might not be in future, but itâs still very experimental, works better for some things than others, requires deep understanding of mathematics.
I work in this area and everyone I know working in it either has a doctorate or doesnât have a doctorate but doesnât touch the maths parts.
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u/Maleficent_Play1092 4d ago
Could you give some overview of what you are doing in work?
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u/Destiny7655 3d ago
I work as a research engineer at a large company in the engineering space who do a variety of industrial software through design, simulation, manufacturing, etc.
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u/HowmanyDans 4d ago
I agree to a point but there are commercial tools branching into this arena that offer similar UI levels of knowledge to implement ML as DoE. An example I have used is PhysicsAI within what was Altair and is now Siemens. Their reduced order AI model was starting to build some real capability but I've not visited it for a while. I'm also aware of DS 3DExperience containing similar toolsets. Their DoE tools have claimed to use ML for decades.
These tools have been approachable for someone that is familiar with FEA/CFD. Understanding what is going on under there hood does take it to the next level.
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u/Destiny7655 3d ago
Thatâs quite different though because the research has already been done, it just sits inside those companies to make accessible tools. That was what I meant by âit might not be in futureâ
The reality is though that unless itâs in a professional tool, people arenât going to do more than early design validation with it before handing it over to a licensed/chartered engineer who signs off on it after running proper simulation studies and building prototype and doing physical testing. This is what I think many people in the AI world miss from real world engineering.
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u/NotTzarPutin 3d ago
The barrier to entry really isnât that high. Geometric deep learning isnât that complex.
Find out if your data type is supported, train on five simulations, experiment.
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u/Destiny7655 3d ago
This is prime dunning Kruger at work. Implementing the ML part is not particularly difficult. Making sure itâs learnt the right thing is.
What about material interfaces? What about non-linear problems and whether a smooth solution is generated dropping non-linearities? What about conservation law enforcement? These are all massive open research areas.
Plugging 5 simulation results into PyTorch will not give better results than non-ML approaches like reduced order models.
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u/ImNotSanguinius 3d ago
In all honesty, most of the time OptiSlang wouldnât be used. Ive only ever seen it used in crazy niche situations. Itâs definitely more important to learn Python so you can do PyAnsys. That would let you do what OptiSlang does with some extra work, plus handle stuff like making documentation and plots.
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u/Safe-Drama9784 3d ago
Absolutely. Python has a huge library and you can do a lot interesting tools based on your need. I've been developing a 3D CAD based on python
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u/Solid-Sail-1658 3d ago
Python is very useful if you want to automate tasks or if you want to learn AI/ML. If you use FEA on a daily basis, I recommend learning Python.
With regard to AI/ML. I first started with gradient based optimization, sensitivity analysis and DOE. Then I moved on to Space Filling design, e.g. Latin Hypercube, Gaussian processes (Kriging) for surrogate modeling and Bayesian Optimization to automatically optimize.
I think if you are performing nonlinear FEA, you should definitely be familiar AI/ML.
If you are just performing linear FEA, you can ignore AI/ML for now. For linear FEA existing approaches such as gradient based optimization or using a 1st order Taylor series approximation will get you really far.
Existing gradient based optimization can handle thousands of design variables with a cost of 10-50 FE solver runs. AI/ML might require millions of runs for thousands of design variables. The high cost of AI/ML makes it impractical for linear FEA.
Given the high cost of AI/ML, I recommend using AI/ML for nonlinear FEA with at most 10 parameters. And don't just use surrogate models, couple surrogate models with Bayesian Optimization or some other procedure to automate the optimization. If you just use surrogate models, you might find your self trapped in a perpetual cycle of trying to improve the accuracy of surrogate models. Avoid this and try to automatically optimize.
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u/TheBlack_Swordsman 4d ago
It depends what you want and need to do.
I don't know python but I have used ChatGPT to help me write over dozens of scripts to automate results,Aegon tables, etc.
I do wish to learn Python because it is a good thing to know, but I am amazed at how easy it has been for me to script and saves 100s of hours of clicking and collecting results.
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u/MIGoneCamping 4d ago
Is it worth learning a general purpose programming language?
Yeah, I would still learn python even with LLMs doing a lot of heavy lifting. Python is incredibly useful for any number of tasks.