@INPROCEEDINGS{10993227,
  author={Ma, Pingchuan and Gao, Zhengqi and Begovic, Amir and Zhang, Meng and Yang, Haoyu and Ren, Haoxing and Huang, Rena and Boning, Duane S. and Gu, Jiaqi},
  booktitle={2025 Design, Automation & Test in Europe Conference (DATE)}, 
  title={BOSON−1: Understanding and Enabling Physically-Robust Photonic Inverse Design with Adaptive Variation-Aware Subspace Optimization}, 
  year={2025},
  volume={},
  number={},
  pages={1-7},
  keywords={Performance evaluation;Optical losses;Runtime;Navigation;Optimization methods;Robustness;Topology;Numerical models;Optimization;Photonics},
  doi={10.23919/DATE64628.2025.10993227}}

@article{10.1063/5.0242728,
  author = {Ma, Pingchuan and Yang, Haoyu and Gao, Zhengqi and Boning, Duane S. and Gu, Jiaqi},
  title = {PIC2O-Sim: A physics-inspired causality-aware dynamic convolutional neural operator for ultra-fast photonic device time-domain simulation},
  journal = {APL Photonics},
  volume = {10},
  number = {3},
  pages = {036104},
  year = {2025},
  month = {03},
  abstract = {Optical simulation plays an important role in photonic hardware design flow. The finite-difference time-domain (FDTD) method is widely adopted to solve time-domain Maxwell equations. However, FDTD is known for its prohibitive runtime cost as it iteratively solves Maxwell equations and takes minutes to hours to simulate a single device. Recently, AI has been applied to realize orders-of-magnitude speedup in partial differential equation solving. However, AI-based FDTD solvers for photonic devices have not been clearly formulated. Directly applying off-the-shelf models to predict the optical field dynamics shows unsatisfying fidelity and efficiency since the model primitives are agnostic to the unique physical properties of Maxwell equations and lack algorithmic customization. In this work, we thoroughly investigate the synergy between neural operator designs and the physical property of Maxwell equations and introduce a physics-inspired AI-based FDTD prediction framework PIC2O-Sim. PIC2O-Sim features a causality-aware dynamic convolutional neural operator as its backbone model that honors the space–time causality constraints via careful receptive field configuration and explicitly captures the permittivity-dependent light propagation behavior via an efficient dynamic convolution operator. Meanwhile, we explore the trade-offs among prediction scalability, fidelity, and efficiency via a multi-stage partitioned time-bundling technique in autoregressive prediction. Multiple key techniques have been introduced to mitigate iterative error accumulation while maintaining efficiency advantages during autoregressive field prediction. Extensive evaluations on three challenging photonic device simulation tasks have shown the superiority of our PIC2O-Sim method, showing 51.2\% lower roll-out prediction error, 23.5 times fewer parameters than state-of-the-art neural operators, providing 133–310× or 31–89× higher simulation speed than an open-source single-process or eight-process parallel FDTD numerical solver.},
  issn = {2378-0967},
  doi = {10.1063/5.0242728},
  url = {https://doi.org/10.1063/5.0242728},
  eprint = {https://pubs.aip.org/aip/app/article-pdf/doi/10.1063/5.0242728/20420133/036104\_1\_5.0242728.pdf},
}

@misc{ma2025sp2rintspatiallydecoupledphysicsinspiredprogressive,
  title={SP2RINT: Spatially-Decoupled Physics-Inspired Progressive Inverse Optimization for Scalable, PDE-Constrained Meta-Optical Neural Network Training}, 
  author={Pingchuan Ma and Ziang Yin and Qi Jing and Zhengqi Gao and Nicholas Gangi and Boyang Zhang and Tsung-Wei Huang and Zhaoran Huang and Duane S. Boning and Yu Yao and Jiaqi Gu},
  year={2025},
  eprint={2505.18377},
  archivePrefix={arXiv},
  primaryClass={physics.optics},
  url={https://arxiv.org/pdf/2505.18377}, 
}