MS student, ETHZ - ETH Zurich
1 paper at NeurIPS 2025
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.