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 ## Papers on PINN Accerleration
 1. **Self-adaptive loss balanced Physics-informed neural networks for the incompressible Navier-Stokes equations**, *Zixue Xiang, Wei Peng, Xiaohu Zheng, Xiaoyu Zhao, Wen Yao*, arXiv:2104.06217 [physics], 2021. [[paper](https://arxiv.org/pdf/2104.06217)]
 2. **A Dual-Dimer method for training physics-constrained neural networks with minimax architecture**, *Dehao Liu, Yan Wang*, Neural Networks, 2021. [[paper](https://www.sciencedirect.com/science/article/abs/pii/S0893608020304536)]
-3. **Adversarial Multi-task Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations**, *Pongpisit Thanasutives, Masayuki Numao, Ken-ichi Fukui*, arXiv:2104.14320 [cs, math], 2021. [[paper](https://arxiv.org/pdf/2104.14320)]
+3. **Adversarial Multi-task Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations**, *Pongpisit Thanasutives, Masayuki Numao, Ken-ichi Fukui*, arXiv:2104.14320 [cs, math], 2021. [[paper](https://arxiv.org/pdf/2104.14320)][[code](https://github.com/Pongpisit-Thanasutives/Physics-Informed-Neural-Networks-Multitask-Learning)]
 4. **DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation**, *Jungeun Kim, Kookjin Lee, Dongeun Lee, Sheo Yon Jin, Noseong Park*, AAAI, 2021. [[paper](https://www.aaai.org/AAAI21Papers/AAAI-4849.KimJ.pdf)]
 5. **Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems**, *Jeremy Yu, Lu Lu, Xuhui Meng, George Em Karniadakis*, Arxiv, 2021.  [[paper](https://arxiv.org/abs/2111.02801)]
 6. **CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method**, *Pao-Hsiung Chiu, Jian Cheng Wong, Chinchun Ooi, My Ha Dao, Yew-Soon Ong*, Arxiv, 2021. [[paper](https://arxiv.org/abs/2110.15832)]
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 1. **Physically guided deep learning solver for time-dependent Fokker–Planck equation**, *Yang Zhang, Ka-Veng Yuen*, International Journal of Non-Linear Mechanics, 2022. [[paper](https://www.sciencedirect.com/science/article/pii/S0020746222001792)]
 1. **A Physically Consistent Framework for Fatigue Life Prediction using Probabilistic Physics-Informed Neural Network**, *Taotao Zhou, Shan Jiang, Te Han, Shun-Peng Zhu, Yinan Cai*, International Journal of Fatigue, 2022. [[paper](https://www.sciencedirect.com/science/article/pii/S0142112322004844)]
 1. **Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks**, *Danial Amini, Ehsan Haghighat, Ruben Juanes*, **Arxiv**, 2022. [[paper](http://arxiv.org/pdf/2209.03276.pdf)][[code](https://github.com/sciann/sciann-applications/tree/master/SciANN-PoroElasticity))]
+1. **Noise-aware physics-informed machine learning for robust PDE discovery**, *Pongpisit Thanasutives, Takashi Morita, Masayuki Numao, Ken-ichi Fukui*, Machine Learning: Science and Technology, 2023. [[paper](https://dx.doi.org/10.1088/2632-2153/acb1f0)][[code](https://github.com/nPIML-team/nPIML)]
 
 ## Papers on PINN Analysis
 1. **Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs**, *Siddhartha Mishra, Roberto Molinaro*, IMA Journal of Numerical Analysis, 2021. [[paper](https://academic.oup.com/imajna/advance-article-abstract/doi/10.1093/imanum/drab032/6297946)]