物理驱动深度学习(PINN)代码

主要总结了论文相关代码

物理驱动深度学习代码

  • Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations 用内嵌物理信息的神经网络求解PDE的源头文章,从数据驱动角度提出PINN,求解PDE正逆问题。代码链接。
  • DGM, (DGM: A deep learning algorithm for solving partial differential equations): A deep learning algorithm for solving partial differential equations:对于高维PDE用数值方法不能求解,提出DGM方法即用神经网络求解高维PDE问题,同时提出用Monte Carlo估计方法代替PDE中的二阶导数能够加速学习。代码链接。
  • hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition:采用变分区域的方式对区域内采点,这是一种启发式的踩点方法,在准确性上有所提升。代码链接。
  • Temperature field inversion of heat-source systems via physics-informed neural network:提出了基于内嵌物理知识神经网络的温度场重构算法。该方法能够减少温度场重构对测点数量的依赖,利用少量测点准确重构温度场。同时改论文提出了一种基于条件数的测点选择方法,能够提升重构模型的鲁棒性。代码链接。
  • Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations。代码链接。
  • PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain:代码链接,python。
  • Physics-Informed Neural Networks for Power Systems:代码链接,python。
  • Physics-informed neural network (PINN) for solving fluid dynamics problems:代码链接。
  • Adversarial Uncertainty Quantification in Physics-Informed Neural Networks:代码链接。
  • Accelerating Training PINN with Prior Dictionary:从网络角度引入先验字典对PINN进行改进,加速训练。代码链接。

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