{"slug": "practical-experience-with-ml-surrogates-for-cfd-and-fea-simulations", "title": "Practical experience with ML surrogates for CFD and FEA simulations?", "summary": "An engineer seeking real-world experience with ML surrogates for CFD and FEA simulations asks about practical architectures, data efficiency, physics-informed approaches, and generalization. The question highlights the tradeoff between speed and accuracy, noting that expensive solver runs remain necessary when surrogates fail.", "body_md": "I work in engineering simulation (CFD and FEA) and I’m increasingly interested in using ML to cut down the cost of expensive solver runs. The traditional loop — mesh, solve, post-process — can take hours per design iteration, and I’d love to hear from people who’ve actually deployed ML surrogates in this space rather than just read the papers.\n\nA few things I’m trying to get a realistic picture of:\n\n**Architectures:** For predicting fields (temperature, pressure, stress) over a geometry, what’s worked better in practice — graph neural networks on mesh data, Fourier Neural Operators, point-cloud approaches, or plain MLP/CNN surrogates on parameterized designs?\n**Data efficiency:** Simulation data is expensive to generate. How few training samples have people gotten away with for a useful surrogate, and does transfer learning across similar geometries help?\n**Physics-informed approaches:** Has anyone found PINNs practical for real engineering geometries, or are they still mostly a research curiosity vs. data-driven surrogates?\n**Generalization:** The hard part is a model that holds up on geometries/boundary conditions outside the training distribution. What’s worked for keeping surrogates trustworthy there?\n\nInterested in real-world experience — what gave you a usable accuracy-vs-speed tradeoff, and where did ML surrogates fall down and force you back to the full solver?", "url": "https://wpnews.pro/news/practical-experience-with-ml-surrogates-for-cfd-and-fea-simulations", "canonical_source": "https://discuss.huggingface.co/t/practical-experience-with-ml-surrogates-for-cfd-and-fea-simulations/177229#post_1", "published_at": "2026-06-29 06:52:09+00:00", "updated_at": "2026-06-29 07:10:42.619556+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "ai-tools", "ai-infrastructure", "ai-ethics"], "entities": ["CFD", "FEA", "graph neural networks", "Fourier Neural Operators", "PINNs", "MLP", "CNN"], "alternates": {"html": "https://wpnews.pro/news/practical-experience-with-ml-surrogates-for-cfd-and-fea-simulations", "markdown": "https://wpnews.pro/news/practical-experience-with-ml-surrogates-for-cfd-and-fea-simulations.md", "text": "https://wpnews.pro/news/practical-experience-with-ml-surrogates-for-cfd-and-fea-simulations.txt", "jsonld": "https://wpnews.pro/news/practical-experience-with-ml-surrogates-for-cfd-and-fea-simulations.jsonld"}}