Neural-HSS: Hierarchical Semi-Separable Neural PDE Solver

Abstract

Deep learning-based methods have shown remarkable effectiveness in solving PDEs, largely due to their ability to enable fast simulations once trained. However, despite the availability of high-performance computing infrastructure, many critical applications remain constrained by the substantial computational costs associated with generating large-scale, high-quality datasets and training models. In this work, inspired by studies on the structure of Green’s functions for elliptic PDEs, we introduce Neural-HSS, a parameter-efficient architecture built upon the Hierarchical Semi-Separable (HSS) matrix structure that is provably data-efficient for a broad class of PDEs. We theoretically analyze the proposed architecture, proving that it satisfies exactness properties even in very low-data regimes. We also investigate its connections with other architectural primitives, such as the Fourier neural operator layer and convolutional layers. We experimentally validate the data efficiency of Neural-HSS on the three-dimensional Poisson equation over a grid of two million points, demonstrating its superior ability to learn from data generated by elliptic PDEs in the low-data regime while outperforming baseline methods. Finally, we demonstrate its capability to learn from data arising from a broad class of PDEs in diverse domains, including electromagnetism, fluid dynamics, and biology.

Please cite this paper as:

@article{sittoni2026neuralhss,
  title={Neural-HSS: Hierarchical Semi-Separable Neural PDE Solver},
  author={Sittoni, Pietro and Zangrando, Emanuele and Casulli, Angelo A. and Guglielmi, Nicola and Tudisco, Francesco},
  journal={arXiv preprint arXiv:2602.18248},
  year={2026}
}

Keywords: neural PDE solvers hierarchical semi-separable matrices Green's functions data efficiency deep learning AI4Science