idrl.bib

# Shared bib file of IDRL.

# 2019

@article{zhang2019multisource,
  title={Multisource selective transfer framework in multiobjective optimization problems},
  author={Zhang, Jun and Zhou, Weien and Chen, Xianqi and Yao, Wen and Cao, Lu},
  journal={IEEE Transactions on Evolutionary Computation},
  volume={24},
  number={3},
  pages={424--438},
  year={2019},
  publisher={IEEE}
}


# 2020

@article{chen2020heat,
  title={The heat source layout optimization using deep learning surrogate modeling},
  author={Chen, Xiaoqian and Chen, Xianqi and Zhou, Weien and Zhang, Jun and Yao, Wen},
  journal={Structural and Multidisciplinary Optimization},
  volume={62},
  number={6},
  pages={3127--3148},
  year={2020},
  publisher={Springer}
}

@article{sun2020deep,
  title={A deep learning-based method for heat source layout inverse design},
  author={Sun, Jialiang and Zhang, Jun and Zhang, Xiaoya and Zhou, Weien},
  journal={IEEE Access},
  volume={8},
  pages={140038--140053},
  year={2020},
  publisher={IEEE}
}

@article{peng2020accelerating,
  title={Accelerating physics-informed neural network training with prior dictionaries},
  author={Peng, Wei and Zhou, Weien and Zhang, Jun and Yao, Wen},
  journal={arXiv preprint arXiv:2004.08151},
  year={2020}
}

# 2021

@article{chen2021deep,
  title={A deep neural network surrogate modeling benchmark for temperature field prediction of heat source layout},
  author={Chen, Xianqi and Zhao, Xiaoyu and Gong, Zhiqiang and Zhang, Jun and Zhou, Weien and Chen, Xiaoqian and Yao, Wen},
  journal={Science China Physics, Mechanics \& Astronomy},
  volume={64},
  number={11},
  pages={1--30},
  year={2021},
  publisher={Springer}
}

@article{zhang2021tonr,
  title={{TONR}: An exploration for a novel way combining neural network with topology optimization},
  author={Zhang, Zeyu and Li, Yu and Zhou, Weien and Chen, Xiaoqian and Yao, Wen and Zhao, Yong},
  journal={Computer Methods in Applied Mechanics and Engineering},
  volume={386},
  pages={114083},
  year={2021},
  publisher={Elsevier}
}

@article{zhao2021surrogate,
  title={A surrogate model with data augmentation and deep transfer learning for temperature field prediction of heat source layout},
  author={Zhao, Xiaoyu and Gong, Zhiqiang and Zhang, Jun and Yao, Wen and Chen, Xiaoqian},
  journal={Structural and Multidisciplinary Optimization},
  volume={64},
  number={4},
  pages={2287--2306},
  year={2021},
  publisher={Springer}
}

@Article{luo2021improved,
AUTHOR = {Jiaxiang Luo, Yu Li, Weien Zhou, Zhiqiang Gong, Zeyu Zhang, Wen Yao},
TITLE = {An Improved Data-Driven Topology Optimization Method Using Feature Pyramid Networks with Physical Constraints},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {128},
YEAR = {2021},
NUMBER = {3},
PAGES = {823--848},
URL = {http://www.techscience.com/CMES/v128n3/44011},
ISSN = {1526-1506},
DOI = {10.32604/cmes.2021.016737}
}


@inproceedings{luo2021novel,
  title={A Novel Method to Accelerate the Solution of Compliance Using Deep Learning for Topology Optimization},
  author={Luo, Jiaxiang and Li, Yu and Zhou, Weien and Chen, Xianqi and Yao, Wen},
  booktitle={International Conference on Mechanical Design},
  pages={1781--1792},
  year={2021},
  organization={Springer}
}

@article{chen2021effective,
  title={An effective gradient and geometry enhanced sequential sampling approach for Kriging modeling},
  author={Chen, Xiaoqian and Zhang, Yi and Zhou, Weien and Yao, Wen},
  journal={Structural and Multidisciplinary Optimization},
  volume={64},
  number={6},
  pages={3423--3438},
  year={2021},
  publisher={Springer}
}

@inproceedings{liu2021difference,
  title={The Difference Coefficient Matrix Condition Number Based Sensor Placement Optimization Method for Robust Temperature Field Reconstruction},
  author={Liu, Xu and Peng, Wei and Liu, Shulei and Gong, Zhiqiang and Yao, Wen},
  booktitle={International Conference on Mechanical Design},
  pages={1721--1739},
  year={2021},
  organization={Springer}
}

@article{xia2021hatchensemble,
  title={HatchEnsemble: an efficient and practical uncertainty quantification method for deep neural networks},
  author={Xia, Yufeng and Zhang, Jun and Jiang, Tingsong and Gong, Zhiqiang and Yao, Wen and Feng, Ling},
  journal={Complex \& Intelligent Systems},
  volume={7},
  number={6},
  pages={2855--2869},
  year={2021},
  publisher={Springer}
}

@article{peng2021idrlnet,
  title={IDRLnet: A physics-informed neural network library},
  author={Peng, Wei and Zhang, Jun and Zhou, Weien and Zhao, Xiaoyu and Yao, Wen and Chen, Xiaoqian},
  journal={arXiv preprint arXiv:2107.04320},
  year={2021}
}

# 2022

@ARTICLE{li2022approximated,
  author={Li, Chao and Wang, Handing and Zhang, Jun and Yao, Wen and Jiang, Tingsong},
  journal={IEEE Transactions on Evolutionary Computation}, 
  title={An Approximated Gradient Sign Method Using Differential Evolution For Black-box Adversarial Attack}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TEVC.2022.3151373}}

@inproceedings{wang2022fca,
  title={{FCA}: Learning a 3d full-coverage vehicle camouflage for multi-view physical adversarial attack},
  author={Wang, Donghua and Jiang, Tingsong and Sun, Jialiang and Zhou, Weien and Gong, Zhiqiang and Zhang, Xiaoya and Yao, Wen and Chen, Xiaoqian},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={36},
  number={2},
  pages={2414--2422},
  year={2022}
}

@article{liu2022temperature,
	title = {Temperature field inversion of heat-source systems via physics-informed neural networks},
	volume = {113},
	issn = {09521976},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S095219762200135X},
	doi = {10.1016/j.engappai.2022.104902},
	abstract = {Temperature field inversion of heat-source systems (TFI-HSS) with limited observations is essential to monitor the system health. Although some methods such as interpolation have been proposed to solve TFI-HSS, those existing methods ignore correlations between data constraints and physics constraints, causing the low precision. In this work, we develop a physics-informed neural network-based temperature field inversion (PINN-TFI) method to solve the TFI-HSS task and a coefficient matrix condition number based position selection of observations (CMCN-PSO) method to select optimal positions of noisy observations. For the TFI-HSS task, the PINN-TFI method encodes constrain terms into the loss function and thus the task is transformed into an optimization problem of minimizing the loss function. In addition, we have found that noise significantly affect reconstruction performances of the PINN-TFI method. To alleviate the effect of noises in observations, we propose the CMCN-PSO method to find optimal positions, where the condition number of observations is used to evaluate positions. The results demonstrate that the PINN-TFI method can significantly improve prediction precisions and the CMCN-PSO method can find good positions to improve the robustness of the PINN-TFI method.},
	language = {en},
	urldate = {2022-07-01},
	journal = {Engineering Applications of Artificial Intelligence},
	author = {Liu, Xu and Peng, Wei and Gong, Zhiqiang and Zhou, Weien and Yao, Wen},
	month = aug,
	year = {2022},
	pages = {104902},
	file = {Liu et al. - 2022 - Temperature field inversion of heat-source systems.pdf:D\:\\zotero_file\\storage\\YHA9HW8D\\Liu et al. - 2022 - Temperature field inversion of heat-source systems.pdf:application/pdf},
}


@article{liu2022novel,
	title = {A novel meta-learning initialization method for physics-informed neural networks},
	issn = {0941-0643, 1433-3058},
	url = {https://link.springer.com/10.1007/s00521-022-07294-2},
	doi = {10.1007/s00521-022-07294-2},
	abstract = {Physics-informed neural networks (PINNs) have been widely used to solve various scientific computing problems. However, large training costs limit PINNs for some real-time applications. Although some works have been proposed to improve the training efficiency of PINNs, few consider the influence of initialization. To this end, we propose a New Reptile initialization-based Physics-Informed Neural Network (NRPINN). The original Reptile algorithm is a metalearning initialization method based on labeled data. PINNs can be trained with less labeled data or even without any labeled data by adding partial differential equations (PDEs) as a penalty term into the loss function. Inspired by this idea, we propose the new Reptile initialization to sample more tasks from the parameterized PDEs and adapt the penalty term of the loss. The new Reptile initialization can acquire initialization parameters from related tasks by supervised, unsupervised, and semi-supervised learning. Then, PINNs with initialization parameters can efficiently solve PDEs. Besides, the new Reptile initialization can also be used for the variants of PINNs. Finally, we demonstrate and verify the NRPINN considering both forward problems, including solving Poisson, Burgers, and Schro¨dinger equations, as well as inverse problems, where unknown parameters in the PDEs are estimated. Experimental results show that the NRPINN training is much faster and achieves higher accuracy than PINNs with other initialization methods.},
	language = {en},
	urldate = {2022-05-27},
	journal = {Neural Computing and Applications},
	author = {Liu, Xu and Zhang, Xiaoya and Peng, Wei and Zhou, Weien and Yao, Wen},
	month = may,
	year = {2022},
	file = {Liu et al. - 2022 - A novel meta-learning initialization method for ph.pdf:D\:\\zotero_file\\storage\\K26XA56N\\Liu et al. - 2022 - A novel meta-learning initialization method for ph.pdf:application/pdf},
}

@article{liu2022surrogate,
  title={A surrogate-assisted evolutionary feature selection algorithm with parallel random grouping for high-dimensional classification},
  author={Liu, Shulei and Wang, Handing and Peng, Wei and Yao, Wen},
  journal={IEEE Transactions on Evolutionary Computation},
  year={2022},
  publisher={IEEE}
}


@article{xiang2022self,
  title={Self-adaptive loss balanced Physics-informed neural networks},
  author={Xiang, Zixue and Peng, Wei and Liu, Xu and Yao, Wen},
  journal={Neurocomputing},
  volume={496},
  pages={11--34},
  year={2022},
  publisher={Elsevier}
}

@article{xiang2022hybrid,
  title={Hybrid Finite Difference with the Physics-informed Neural Network for solving PDE in complex geometries},
  author={Xiang, Zixue and Peng, Wei and Zhou, Weien and Yao, Wen},
  journal={arXiv preprint arXiv:2202.07926},
  year={2022}
}

@article{xiang2021self,
  title={Self-adaptive loss balanced physics-informed neural networks for the incompressible navier-stokes equations},
  author={Xiang, Zixue and Peng, Wei and Zheng, Xiaohu and Zhao, Xiaoyu and Yao, Wen},
  journal={arXiv preprint arXiv:2104.06217},
  year={2021}
}

@article{peng2022rang,
  title={RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networks},
  author={Peng, Wei and Zhou, Weien and Zhang, Xiaoya and Yao, Wen and Liu, Zheliang},
  journal={arXiv preprint arXiv:2205.01051},
  year={2022}
}

@article{liu2022bayesian,
  title={Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data},
  author={Liu, Xu and Yao, Wen and Peng, Wei and Zhou, Weien},
  journal={arXiv preprint arXiv:2205.06948},
  year={2022}
}

@article{bao2022physics,
  title={A physics and data co-driven surrogate modeling approach for temperature field prediction on irregular geometric domain},
  author={Bao, Kairui and Yao, Wen and Zhang, Xiaoya and Peng, Wei and Li, Yu},
  journal={arXiv preprint arXiv:2203.08150},
  year={2022}
}

@article{sun2022searching,
  title={Searching for Robust Neural Architectures via Comprehensive and Reliable Evaluation},
  author={Sun, Jialiang and Jiang, Tingsong and Li, Chao and Zhou, Weien and Zhang, Xiaoya and Yao, Wen and Chen, Xiaoqian},
  journal={arXiv preprint arXiv:2203.03128},
  year={2022}
}

@article{zheng2022deep,
  title={Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction},
  author={Zheng, Xiaohu and Yao, Wen and Gong, Zhiqiang and Zhang, Yunyang and Zhao, Xiaoyu and Jiang, Tingsong},
  journal={arXiv preprint arXiv:2202.06596},
  year={2022}
}