Most cited papers in Model Reduction and Neural Networks
What are the most cited papers in Model Reduction and Neural Networks?
The most cited paper in Model Reduction and Neural Networks is "Greedy function approximation: A gradient boosting machine." (Jerome H. Friedman, 2001), with 30,593 citations. Next come "Multilayer feedforward networks are universal approximators" (21,792) and "Reducing the Dimensionality of Data with Neural Networks" (21,368).
Showing 50 of 100 papers. Every paper with more than 1,035 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Greedy function approximation: A gradient boosting machine. Jerome H. Friedman · The Annals of Statistics | Oct 2001 | 30,593 |
| 2 | Multilayer feedforward networks are universal approximators Kurt Hornik, Maxwell B. Stinchcombe, Halbert White · Neural Networks | 1989 | 21,792 |
| 3 | Reducing the Dimensionality of Data with Neural Networks Geoffrey E. Hinton, Ruslan Salakhutdinov · Science | 27 Jul 2006 | 21,368 |
| 4 | 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 | 3 Nov 2018 | 19,776 |
| 5 | Physics-informed machine learning George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu et al. · Nature Reviews Physics | 24 May 2021 | 8,059 |
| 6 | Overcoming catastrophic forgetting in neural networks James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz et al. · Proceedings of the National Academy of Sciences | 14 Mar 2017 | 7,777 |
| 7 | Continuous control with deep reinforcement learning Timothy Lillicrap, Jonathan J. Hunt, Alexander Pritzel et al. · arXiv (Cornell University) | 22 Jul 2016 | 6,781 |
| 8 | Dynamic mode decomposition of numerical and experimental data Peter J. Schmid · Journal of Fluid Mechanics | Jul 2010 | 5,995 |
| 9 | Denoising Diffusion Probabilistic Models Ho, Jonathan, Ajay N. Jain, Pieter Abbeel · arXiv (Cornell University) | 19 Jun 2020 | 5,696 |
| 10 | State-space solutions to standard H/sub 2/ and H/sub infinity / control problems John C. Doyle, K. Glover, Pramod P. Khargonekar et al. · IEEE Transactions on Automatic Control | 1989 | 5,567 |
| 11 | The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations Dongbin Xiu, George Em Karniadakis · SIAM Journal on Scientific Computing | 2002 | 4,874 |
| 12 | Discovering governing equations from data by sparse identification of nonlinear dynamical systems Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz · Proceedings of the National Academy of Sciences | 28 Mar 2016 | 4,849 |
| 13 | Adding Conditional Control to Text-to-Image Diffusion Models Lvmin Zhang, Anyi Rao, Maneesh Agrawala | Oct 2023 | 4,229 |
| 14 | On the difficulty of training Recurrent Neural Networks Razvan Pascanu, Tomáš Mikolov, Yoshua Bengio · arXiv (Cornell University) | 21 Nov 2012 | 3,790 |
| 15 | Neural Networks and the Bias/Variance Dilemma Stuart Geman, Elie Bienenstock, René Doursat · Neural Computation | 1992 | 3,629 |
| 16 | Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators Lu Lu, Pengzhan Jin, Guofei Pang et al. · Nature Machine Intelligence | 18 Mar 2021 | 3,345 |
| 17 | Networks for approximation and learning Tomaso Poggio, Federico Girosi · Proceedings of the IEEE | 1990 | 3,276 |
| 18 | Universal approximation bounds for superpositions of a sigmoidal function Andrew R. Barron · IEEE Transactions on Information Theory | May 1993 | 2,899 |
| 19 | All optimal Hankel-norm approximations of linear multivariable systems and their L , ∞ -error bounds† K. Glover · International Journal of Control | Jun 1984 | 2,882 |
| 20 | Machine Learning for Fluid Mechanics Steven L. Brunton, Bernd R. Noack, Petros Koumoutsakos · Annual Review of Fluid Mechanics | 12 Sep 2019 | 2,842 |
| 21 | A Practical Guide to Training Restricted Boltzmann Machines Geoffrey E. Hinton · Lecture notes in computer science | 2012 | 2,838 |
| 22 | An eigensystem realization algorithm for modal parameter identification and model reduction Jer-Nan Juang, Richard S. Pappa · Journal of Guidance Control and Dynamics | Sep 1985 | 2,780 |
| 23 | Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next Salvatore Cuomo, Vincenzo Schiano Di Cola, Fabio Giampaolo et al. · Journal of Scientific Computing | 26 Jul 2022 | 2,714 |
| 24 | An Introduction to Variational Autoencoders Diederik P. Kingma, Max Welling · Foundations and Trends® in Machine Learning | 28 Nov 2019 | 2,652 |
| 25 | Generative Adversarial Networks Iddo Drori · Cambridge University Press eBooks | 18 Aug 2022 | 2,579 |
| 26 | Application of the Karhunen-Loeve procedure for the characterization of human faces Michael J. L. Kirby, L. Sirovich · IEEE Transactions on Pattern Analysis and Machine Intelligence | 1990 | 2,461 |
| 27 | Improved numerical dissipation for time integration algorithms in structural dynamics Hans M. Hilber, Thomas J.R. Hughes, Robert L. Taylor · Earthquake Engineering & Structural Dynamics | Jul 1977 | 2,436 |
| 28 | Artificial neural networks for solving ordinary and partial differential equations I.E. Lagaris, A. Likas, D.I. Fotiadis · IEEE Transactions on Neural Networks | 1998 | 2,402 |
| 29 | On the momentum term in gradient descent learning algorithms Ning Qian · Neural Networks | 1999 | 2,376 |
| 30 | Spectral analysis of nonlinear flows Clarence W. Rowley, Igor Mezić, Shervin Bagheri et al. · Journal of Fluid Mechanics | 18 Nov 2009 | 2,348 |
| 31 | Pymoo: Multi-Objective Optimization in Python Julian Blank, Kalyanmoy Deb · IEEE Access | 2020 | 2,341 |
| 32 | DGM: A deep learning algorithm for solving partial differential equations Justin A. Sirignano, Konstantinos Spiliopoulos · Journal of Computational Physics | 24 Aug 2018 | 2,270 |
| 33 | Diffusion Models Beat GANs on Image Synthesis Prafulla Dhariwal, Alex Nichol · arXiv (Cornell University) | 11 May 2021 | 2,191 |
| 34 | Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations Maziar Raissi, Alireza Z. K. Yazdani, George Em Karniadakis · Science | 30 Jan 2020 | 2,145 |
| 35 | Detecting Topological Order in a Ground State Wave Function Michael Levin, Xiao-Gang Wen · Physical Review Letters | 24 Mar 2006 | 2,137 |
| 36 | An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications Esteban P. Samaniego, Cosmin Anitescu, Somdatta Goswami et al. · Computer Methods in Applied Mechanics and Engineering | 16 Jan 2020 | 2,096 |
| 37 | Physics-informed neural networks (PINNs) for fluid mechanics: a review Shengze Cai, Zhiping Mao, Zhicheng Wang et al. · Acta Mechanica Sinica | Dec 2021 | 2,059 |
| 38 | General properties of entropy Alfred Wehrl · Reviews of Modern Physics | Apr 1978 | 1,986 |
| 39 | N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems Peter Van Overschee, Bart De Moor · Automatica | 1994 | 1,985 |
| 40 | A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems Peter Benner, Serkan Gugercin, Karen E. Willcox · SIAM Review | 2015 | 1,948 |
| 41 | A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition Matthew O. Williams, Ioannis G. Kevrekidis, Clarence W. Rowley · Journal of Nonlinear Science | 4 Jun 2015 | 1,929 |
| 42 | Modal Analysis of Fluid Flows: An Overview Kunihiko Taira, Steven L. Brunton, Scott T. M. Dawson et al. · AIAA Journal | 31 Oct 2017 | 1,908 |
| 43 | Diffusion Models in Vision: A Survey Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 27 Mar 2023 | 1,839 |
| 44 | Explicit reversible integrators for extended systems dynamics Glenn J. Martyna, Mark E. Tuckerman, Douglas J. Tobias et al. · Molecular Physics | 10 Apr 1996 | 1,823 |
| 45 | Solving high-dimensional partial differential equations using deep learning Jiequn Han, Arnulf Jentzen, E Weinan · Proceedings of the National Academy of Sciences | 6 Aug 2018 | 1,793 |
| 46 | Reynolds averaged turbulence modelling using deep neural networks with embedded invariance Julia Ling, Andrew J. Kurzawski, Jeremy Alan Templeton · Journal of Fluid Mechanics | 18 Oct 2016 | 1,658 |
| 47 | Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors Auke Jan Ijspeert, Jun Nakanishi, Heiko Hoffmann et al. · Neural Computation | 13 Nov 2012 | 1,625 |
| 48 | Data-driven discovery of partial differential equations Samuel Rudy, Steven L. Brunton, Joshua L. Proctor et al. · Science Advances | 7 Apr 2017 | 1,597 |
| 49 | Discrete mechanics and variational integrators Jerrold Eldon Marsden, Matthew West · Acta Numerica | May 2001 | 1,593 |
| 50 | An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations Maxime Barrault, Yvon Maday, Ngoc Cuong Nguyen et al. · Comptes Rendus Mathématique | 14 Oct 2004 | 1,537 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.