sparse-kernels

This paper presents a local and sparse kernel method for learning solution operators of incompressible flows, using divergence-free local interpolants and compactly supported kernels with Operator Lagrange weights to achieve sparse, GPU-parallel regression that outperforms neural operators on ten fluid benchmarks.