CoolData: Benchmarking Machine Learning Methods for Electronics Cooling

The integration of Machine Learning (ML) into Computer-Aided Engineering (CAE) workflows promises significant speed-ups, particularly in the field of Computational Fluid Dynamics (CFD). However, the development of robust ML models in CFD is hindered by the lack of high-quality, application-specific datasets. This work addresses this gap with CoolData, a large-scale electronics cooling dataset containing $60,848$ stationary 3D flow and temperature fields for a diverse set of geometries, simulated using the industrial CFD solver Simcenter STAR-CCM+. To our knowledge, this is the first high-quality and publicly available dataset for electronics cooling applications. We show how this dataset can be leveraged in an ML pipeline, benchmarking both volumetric models (3D U-Net, DOMINO, Transolver) and surface models (MeshGraphNet, DOMINO, Transolver) across a range of training-set sizes. Beyond in-distribution accuracy, we introduce a systematic out-of-distribution (OOD) study along two axes enabled by CoolData's controlled parametrization: geometric composition (body count and shape held out from training) and thermal extrapolation (peak component temperature beyond the training range).