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Removing repeated reference #7

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Removing repeated reference #7

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3 changes: 1 addition & 2 deletions README.md
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Expand Up @@ -62,11 +62,10 @@ A [script](./ref_convert.py) for converting bibtex to the markdown used in this
3. **PPINN: Parareal physics-informed neural network for time-dependent PDEs**, *Xuhui Meng, Zhen Li, Dongkun Zhang, George Em Karniadakis*, Computer Methods in Applied Mechanics and Engineering, 2020. [[paper](https://arxiv.org/pdf/2104.10013)]
4. **When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?**, *Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi*, arXiv:2109.09444 [cs, math, stat], 2021. [[paper](http://arxiv.org/pdf/2109.09444.pdf)]
5. **Scaling physics-informed neural networks to large domains by using domain decomposition**, *Ben Moseley, Andrew Markham, Tarje Nissen-Meyer*, *NIPS*, 2021. [[paper](https://openreview.net/forum?id=o1WiAZiw_CE)]
6. **Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations**, *Ben Moseley, Andrew Markham, Tarje Nissen-Meyer*, arXiv:2107.07871 [physics], 2021. [[paper](http://arxiv.org/pdf/2107.07871.pdf)]
1. **Improved Deep Neural Networks with Domain Decomposition in Solving Partial Differential Equations**, *Wei Wu, Xinlong Feng, Hui Xu*, Journal of Scientific Computing, 2022. [[paper](https://doi.org/10.1007/s10915-022-01980-y)]
1. **INN: Interfaced neural networks as an accessible meshless approach for solving interface PDE problems**, *Sidi Wu, Benzhuo Lu*, Journal of Computational Physics, 2022. [[paper](https://www.sciencedirect.com/science/article/pii/S0021999122006507)][[code](https://github.com/bzlu-Group/INN)]

## Papers on PINN Accerleration
## Papers on PINN Acceleration
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)]
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