这里记录一些常见的物理信息神经网络（Physics-informed neural networks, PINN）文献。
“文献整理”前置说明：主要参考综述文献、文章的引言，以及使用搜索引擎和文献关联工具等，以早期的引用率高的文献为主，按年份时间进行排列，同年份的不分先后。列表大概率不完整，仅供参考，之后可能不定期补充更新。如果需要阅读更多的或者最新的文献，可自行搜索查找。
一、论文
- 1994 - Dissanayake and Phan-Thien - Neural-network-based approximations for solving partial differential equations https://onlinelibrary.wiley.com/doi/abs/10.1002/cnm.1640100303
- 2018 - Raissi and Karniadakis - Hidden physics models Machine learning of nonlinear partial differential equations https://www.sciencedirect.com/science/article/abs/pii/S0021999117309014
- 2019 - Raissi et al. - Physics-informed neural networks A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations https://www.sciencedirect.com/science/article/abs/pii/S0021999118307125
- 2019 - Yang and Perdikaris - Adversarial uncertainty quantification in physics-informed neural networks https://www.sciencedirect.com/science/article/abs/pii/S0021999119303584
- 2020 - Mao et al. - Physics-informed neural networks for high-speed flows https://www.sciencedirect.com/science/article/abs/pii/S0045782519306814
- 2020 - Sun et al. - Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data https://www.sciencedirect.com/science/article/abs/pii/S004578251930622X
- 2020 - Jagtap et al. - Adaptive activation functions accelerate convergence in deep and physics-informed neural networks https://www.sciencedirect.com/science/article/abs/pii/S0021999119308411
- 2020 - Jagtap et al. - Conservative physics-informed neural networks on discrete domains for conservation laws Applications to forward and inverse problems https://www.sciencedirect.com/science/article/abs/pii/S0045782520302127
- 2020 - Li et al. - Neural Operator Graph Kernel Network for Partial Differential Equations https://arxiv.org/abs/2003.03485
- 2020 - Karniadakis - Extended Physics-Informed Neural Networks (XPINNs) A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations https://global-sci.org/intro/article_detail/cicp/18403.html
- 2021 - Lu et al. - DeepXDE A Deep Learning Library for Solving Differential Equations https://epubs.siam.org/doi/10.1137/19M1274067
- 2021 - Jin et al. - NSFnets (Navier-Stokes flow nets) Physics-informed neural networks for the incompressible Navier-Stokes equations https://www.sciencedirect.com/science/article/abs/pii/S0021999120307257
- 2021 - Yang et al. - B-PINNs Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data https://www.sciencedirect.com/science/article/abs/pii/S0021999120306872
- 2021 - Wang et al. - Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks https://epubs.siam.org/doi/10.1137/20M1318043
- 2021 - Kharazmi et al. - hp-VPINNs Variational physics-informed neural networks with domain decomposition https://www.sciencedirect.com/science/article/abs/pii/S0045782520307325
- 2022 - Wang et al. - When and why PINNs fail to train A neural tangent kernel perspective https://www.sciencedirect.com/science/article/abs/pii/S002199912100663X
- 2022 - Yu et al. - Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems https://www.sciencedirect.com/science/article/abs/pii/S0045782522001438
- ……
二、综述
- 2021 - Karniadakis et al. - Physics-informed machine learning https://www.nature.com/articles/s42254-021-00314-5
- 2021 - Cai et al. - Physics-informed neural networks (PINNs) for fluid mechanics a review https://link.springer.com/article/10.1007/s10409-021-01148-1
- 2022 - Cuomo et al. - Scientific Machine Learning Through Physics–Informed Neural Networks Where we are and What's Next https://link.springer.com/article/10.1007/s10915-022-01939-z
- ……
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