Hi everyone,
I’m trying to build a surrogate model that predicts steady-state temperature fields on a [heat sink / electronics board] from its geometry parameters, so I can skip running a full simulation for every design change.
My setup so far:
• About [150–300] simulation runs, each with [N] geometry parameters (fin height, spacing, base thickness, etc.) • Output is a temperature field on a [structured grid / unstructured mesh with ~X nodes]
• I’ve tried a simple MLP on the parameters, predicting a downsampled field. Mean error is acceptable, but it misses hot spots near sharp features.
With a dataset this small, is it more sensible to go with a Fourier neural operator, a graph-based model, or a CNN on a rasterized geometry? Or should I stay with a simpler model plus a PCA reduction of the field?
If there are open datasets or Hub models for physics surrogates that I could fine-tune or learn from, I’d appreciate pointers. I can share my training loop and a sample of the data if that helps. Thanks!
With 150–300 runs, I would stay with PCA plus a small model for now — an FNO or graph model will likely spend that budget learning the field’s basics, while your current miss has a cheaper cause: a downsampled field trained on mean error can be right on average and still smooth away the peaks. Keep the simple model, but score it on worst-case and 95th-percentile error (and whether the hotspot lands in the right place), and add a second small head that predicts peak temperature and its location, or weight the loss toward high-gradient cells near sharp features. If that still misses, test a CNN on the rasterized geometry on the same held-out geometries — that comparison, not the architecture names, will tell you whether geometry representation is really the bottleneck.