Machine learning for fast and reliable solution of time-dependent differential equations

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

Dropout: a simple way to prevent neural networks from overfitting

Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky et al. · 2014 · 34.2K citations

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Sergey Ioffe · arXiv (Cornell University) · 2024 · 15.6K citations · Full text

Data Augmentation, Deep Neural Networks, Machine Vision +15

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