Weilong Guan (关伟龙)

Weilong Guan (关伟龙)

he/him

Ph.D. Student
State Key Lab of CAD&CG
Zhejiang University

Publications

You can also find my articles on my Google Scholar profile.

ThermoPhoton: Fast 3D Thermal Simulation of Photonic Integrated Circuits via Operator Learning

Published in 2025 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 2025

This paper proposes ThermoPhoton, a fast 3D thermal simulation framework for Photonic Integrated Circuits (PICs) using operator learning, achieving significant speedup over FEM.

Recommended citation: W. Guan, L. Huang, Y. Lin, Y. Wu, Y. Tong, Y. Ma. (2025). "ThermoPhoton: Fast 3D Thermal Simulation of Photonic Integrated Circuits via Operator Learning." 2025 IEEE/ACM International Conference On Computer Aided Design (ICCAD). pp. 1-8.
Download Paper

A Compact Integrated Silicon Photonic Processor for Real-Time Polarization Control

Published in CLEO: Applications and Technology (CLEO 2025), 2025

This paper demonstrates a compact silicon photonic processor capable of real-time polarization control, offering a scalable solution for optical communications and computing.

Recommended citation: Z. Lin, W. Zhou, X. Feng, W. Guan, Y. Ma, Y. Tong. (2025). "A Compact Integrated Silicon Photonic Processor for Real-Time Polarization Control." CLEO: Applications and Technology 2025. pp. AA128_6.
Download Paper

Automatic Routing for Photonic Integrated Circuits Under Delay Matching Constraints

Published in 2025 Design, Automation & Test in Europe Conference (DATE), 2025

This paper presents an automatic routing algorithm for Photonic Integrated Circuits (PICs) that optimizes insertion loss while strictly satisfying delay matching constraints.

Recommended citation: Y. Wu, W. Guan, Y. Tong, Y. Ma. (2025). "Automatic Routing for Photonic Integrated Circuits Under Delay Matching Constraints." 2025 Design, Automation & Test in Europe Conference (DATE). pp. 1-2.
Download Paper

A dimension-augmented physics-informed neural network (DaPINN) with high level accuracy and efficiency

Published in Journal of Computational Physics (JCP), 2023

This paper introduces DaPINN, a novel framework that improves the accuracy and convergence of PINNs for solving high-dimensional multi-physics PDEs in chip interconnects.

Recommended citation: W. Guan, K. Yang, Y. Chen, S. Liao, Z. Guan. (2023). "A dimension-augmented physics-informed neural network (DaPINN) with high level accuracy and efficiency." Journal of Computational Physics. 491: 112360.
Download Paper