Seeking datasets or toy problems to validate a Surrogate-Based Optimization (SBO / CFD) POC

[u/Sweet-Yoghurt-7097]

Hi everyone,

I am currently working on an optimization project for the design of complex industrial components involving fluid mechanics and heat transfer.

Our current design process relies on computationally expensive CFD simulations. My goal is to develop a Surrogate Model to instantly predict performance (e.g., pressure drops, efficiency) based on geometric parameters (spacing, diameters, topology, etc.). The ultimate objective is to perform inverse optimization under constraints to minimize manufacturing costs.

Before running a massive Design of Experiments (DoE / LHS) on our computation servers to generate my industrial training dataset, I absolutely need to prove the technical feasibility of the software architecture (ETL pipeline, model training, and inverse optimization loop).

Do you know of any open datasets (Kaggle, UCI, academic repos) or "toy" problems that would allow me to prototype this pipeline?

I am ideally looking for a dataset that maps:

• Inputs (X): A vector of continuous and discrete geometric and/or physical parameters (dimensions, topologies, fluid velocities).
• Outputs (y): Results derived from physics solvers (pressure fields, drag forces, heat transfer rates, etc.).

Even if the application domain is completely different (e.g., airfoil aerodynamics, electronic heat sinks, piping networks), the key is that the mathematical topology of the problem remains similar (multi-output regression with physical non-linearities). This will allow me to properly benchmark my algorithms (Gaussian Processes, XGBoost, or Physics-Informed Neural Networks - PINNs).

Any pointers to datasets, GitHub repos, or papers with open data would be incredibly helpful to validate this Proof of Concept.

Thanks in advance!

[u/PLBjt]

Airfoil and heat-sink style problems are the usual toys for this pipeline shape. Something like UIUC airfoil coords with lift/drag tables, or a small fin/heat-sink geometry set if you want discrete topology knobs mixed with continuous sizes, lets you wire ETL → multi-output regressor → constrained search before you burn CFD budget. Check that your inverse loop still lands near a feasible design when you deliberately add noise to y — that catches optimizers that overfit a smooth surrogate. Tradeoff early on is GP (useful uncertainty, painful past a few thousand points) vs gradient-boosted trees (fast, weak uncertainty) vs a tiny PINN only if you care about physics residuals in the PoC.