Kolmogorov–Arnold-Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov–Arnold Networks

Deep Residual Learning for Image Recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. · 2016 · 214.9K citations · Full text

Image Classification, Deep Neural Networks, Machine Vision +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

Isogeometric analysis: CAD, finite elements, NURBS, exact geometry and mesh refinement

Thomas J.R. Hughes, J. Austin Cottrell, Yuri Bazilevs · Computer Methods in Applied Mechanics and Engineering · 2005 · 6K citations · Full text

Geometric Modeling, Mesh Optimization, Engineering +12