Journal Of Computational Physics高引用文章

• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

  引用次数：203

• Hidden physics models: Machine learning of nonlinear partial differential equations

  引用次数：86

• The scalar auxiliary variable (SAV) approach for gradient flows

  引用次数：85

• DGM: A deep learning algorithm for solving partial differential equations

  引用次数：76

• Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

  引用次数：49

• Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

  引用次数：45

• A review of level-set methods and some recent applications

  引用次数：43

• A review of definitions of fractional derivatives and other operators

  引用次数：41

• A fast linearized conservative finite element method for the strongly coupled nonlinear fractional Schrodinger equations

  引用次数：41

• Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification

  引用次数：36