PhD Student

Huilin Cao (曹慧琳)

Machine Learning

Huilin Cao is a PhD student whose research focuses on machine learning, dictionary learning, and computational electromagnetism.

General Information

Email Address	caohuilin2022 at sjtu dot edu dot cn
Research Interests	machine learning, dictionary learning, computational electromagnetism

Education

2024.09 - Present

PhD in Mathematics

Institute of Natural Sciences, Shanghai Jiao Tong University
2022.09 - 2024.06

Master in Mathematics

School of Mathematical Sciences, Shanghai Jiao Tong University
2018.09 - 2022.06

Bachelor in Mathematics

School of Mathematics and Statistics, Huazhong University of Science and Technology

Conference

Physics-Augmented Graph Transformer for Multi-step Time-Domain Maxwell’s Equations in Electromagnetic Scattering

Huilin Cao, Haoran Sun, Jingwei Liang, Maokun Li, and Yuxiang Ren

In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, Aug 2026

DOI HTML

@inproceedings{cao2026physicsaugmented,
  title = {Physics-Augmented Graph Transformer for Multi-step Time-Domain Maxwell's Equations in Electromagnetic Scattering},
  author = {Cao, Huilin and Sun, Haoran and Liang, Jingwei and Li, Maokun and Ren, Yuxiang},
  booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2},
  pages = {10532--10542},
  year = {2026},
  month = aug,
  publisher = {ACM},
  doi = {10.1145/3770855.3818937},
}

Journal
Physics-Data Hybrid Driven Edge-Featured Graph Attention Networks for Surface Currents Learning of 3D PEC Targets

Huilin Cao, Haoran Sun, Jingwei Liang, Tao Shan, Yuxiang Ren, and Maokun Li

IEEE Transactions on Antennas and Propagation, 2025

HTML

To achieve fast and accurate modeling of electrically large targets, this paper proposes a physics-data hybrid driven surface current learning method (PdEgatSCL), which can learn combined field integral operator and achieve end-to-end solving from incident fields to surface currents. The edge-feature graph attention network (EGAT) is employed as the foundational architecture of PdEgatSCL, where the Green’s function and its divergence are explicitly encoded as edge features into the network. The attention mechanism of EGAT is leveraged to model the scattering characteristics of the targets. Additionally, the center coordinates and area of the discretized surface elements are input as node features to ensure that the mesh information is fully utilized. Finally, PdEgatSCL is applied to solve the surface currents of three-dimensional perfect electric conductor (PEC) targets. Numerical results demonstrate that the surface current solutions obtained by PdEgatSCL align well with the ground truth computed by the Method of Moments (MoM). Moreover, compared to SOTA AI-based method, PdEgatSCL exhibits higher accuracy while accelerating the average inference time by 87.67% and reducing memory consumption by 99.24%.
@article{cao2025physics, title = {Physics-Data Hybrid Driven Edge-Featured Graph Attention Networks for Surface Currents Learning of 3D PEC Targets}, author = {Cao, Huilin and Sun, Haoran and Liang, Jingwei and Shan, Tao and Ren, Yuxiang and Li, Maokun}, journal = {IEEE Transactions on Antennas and Propagation}, year = {2025}, publisher = {IEEE}, }

General Information

Education

PhD in Mathematics

Master in Mathematics

Bachelor in Mathematics

Physics-Augmented Graph Transformer for Multi-step Time-Domain Maxwell’s Equations in Electromagnetic Scattering

Physics-Data Hybrid Driven Edge-Featured Graph Attention Networks for Surface Currents Learning of 3D PEC Targets