HiDeNN-TD: Reduced-order hierarchical deep learning neural networks

Adam: A Method for Stochastic Optimization

Diederik P. Kingma, Jimmy Ba · UvA-DARE (University of Amsterdam) · 2014 · 84.5K citations · Full text

Artificial Intelligence, Mathematical Programming, Model Optimization +14

Multilayer feedforward networks are universal approximators

Kurt Hornik, Maxwell B. Stinchcombe, Halbert White · Neural Networks · 1989 · 20.7K citations

Evolving Neural Network, Engineering, Machine Learning +5

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

Maziar Raissi, Paris Perdikaris, George Em Karniadakis · Journal of Computational Physics · 2018 · 14.4K citations · Full text

Engineering, Pde-constrained Optimization, Deep Learning Framework +6

Approximation by superpositions of a sigmoidal function

George Cybenko · Mathematics of Control Signals and Systems · 1989 · 13.4K citations

Computational Neuroscience, Approximation Method, Inverse Problems +4

Tensor Decompositions and Applications

Tamara G. Kolda, Brett W. Bader · SIAM Review · 2009 · 10.2K citations

Tensor Toolbox, Representation Theory, Data Science +13