We propose an accurate and efficient neural operator architecture for learning PDE solutions on arbitrary domains. We demonstrate its effectiveness across a variety of challenging benchmarks, including large-scale 3D CFD problems.

This paper advocates for a unified framework for surrogate model training and evaluation to address methodological fragmentation, improve reproducibility, and enable cross-domain collaboration in scientific research.