Publications

PML4SC Publications

4 papers listed

3 accepted papers

2026 latest publication year

2026

conference Accepted

Closing the Capacity-Convergence Gap: Globally Optimal Configuration of Implicit Neural Representations

Sipeng Chen , Yan Zhang , Shibo Li

ECCV 2026 European Conference on Computer Vision 2026

OptiINR closes the capacity-convergence gap in implicit neural representations by formulating activation and initialization configuration as a principled global optimization problem.

arXiv

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BibTeX

@inproceedings{chen2026closing,
  title={Closing the Capacity-Convergence Gap: Globally Optimal Configuration of Implicit Neural Representations},
  author={Chen, Sipeng and Zhang, Yan and Li, Shibo},
  booktitle={European Conference on Computer Vision},
  year={2026}
}

conference Accepted

Regime-Adaptive Bayesian Optimization via Dirichlet Process Mixtures of Gaussian Processes

Yan Zhang , Xuefeng Liu , Sipeng Chen , Sascha Ranftl , Chong Liu , Shibo Li

ICML 2026 Proceedings of the 43rd International Conference on Machine Learning 2026

A regime-adaptive Bayesian optimization framework that models heterogeneous response patterns with Dirichlet process mixtures of Gaussian processes.

arXiv

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BibTeX

@article{zhang2026rambo,
  title={Regime-Adaptive {Bayesian} Optimization via {Dirichlet} Process Mixtures of {Gaussian} Processes},
  author={Zhang, Yan and Liu, Xuefeng and Chen, Sipeng and Ranftl, Sascha and Liu, Chong and Li, Shibo},
  journal={arXiv preprint arXiv:2601.20043},
  year={2026}
}

conference Accepted

COMPOL: A Unified Neural Operator Framework for Scalable Multi-Physics Simulations

Junqi Qu , Tao Wang , Yushun Dong , Hewei Tang , Shibo Li

IJCNN/WCCI 2026 International Joint Conference on Neural Networks, IEEE World Congress on Computational Intelligence 2026

A unified neural operator framework for scalable multi-physics simulations, currently available as an arXiv preprint and accepted by IJCNN 2026.

arXiv

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Code

BibTeX

@article{qu2025compol,
  title={{COMPOL}: A Unified Neural Operator Framework for Scalable Multi-Physics Simulations},
  author={Qu, Junqi and Wang, Tao and Dong, Yushun and Tang, Hewei and Li, Shibo},
  journal={arXiv preprint arXiv:2501.17296},
  year={2025}
}

2025

preprint

Dynamic Bayesian Optimization Framework for Instruction Tuning in Partial Differential Equation Discovery

Junqi Qu , Yan Zhang , Shangqian Gao , Shibo Li

arXiv 2025 arXiv preprint 2025

NeuroSymBO frames instruction tuning for PDE discovery as a sequential decision problem and uses Bayesian optimization to adaptively select reasoning strategies during multi-step generation.

arXiv

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Code

BibTeX

@article{qu2025neurosymbo,
  title={Dynamic {Bayesian} Optimization Framework for Instruction Tuning in Partial Differential Equation Discovery},
  author={Qu, Junqi and Zhang, Yan and Gao, Shangqian and Li, Shibo},
  journal={arXiv preprint arXiv:2601.00088},
  year={2025}
}