文章名稱引用次數(shù)

• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations203
• Hidden physics models: Machine learning of nonlinear partial differential equations86
• The scalar auxiliary variable (SAV) approach for gradient flows85
• DGM: A deep learning algorithm for solving partial differential equations76
• Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data49
• Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification45
• A review of level-set methods and some recent applications43
• A review of definitions of fractional derivatives and other operators41
• A fast linearized conservative finite element method for the strongly coupled nonlinear fractional Schrodinger equations41
• Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification36