Fetching the paper…
Reading the bibliography…
We study the use of feedforward neural networks (FNN) to develop models of nonlinear dynamical systems from data.
Learning internal representations by error propagation
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1985
Earlier work this paper cites.
Extracting qualitative dynamics from experimental data
Dr S Broomhead and Gregory P King · 1986
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
Earlier work this paper cites.
Finding structure in time
Jeffrey L Elman · 1990
Earlier work this paper cites.
Identification and control of dynamical systems using neural networks
Kumpati S Narendra and Kannan Parthasarathy · 1990
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
Earlier work this paper cites.
Identification and control of nonlinear systems using neural network models: Design and stability analysis
Marios M Polycarpou and Petros A Ioannou · 1991
Earlier work this paper cites.
Properties of neural networks with applications to modelling non-linear dynamical systems
SA Billings, HB Jamaluddin, and S Chen · 1992
Earlier work this paper cites.
Forecasting the behavior of multivariate time series using neural networks
Kanad Chakraborty, Kishan Mehrotra, Chilukuri K Mohan, and Sanjay Ranka · 1992
Earlier work this paper cites.
Neural networks and dynamical systems
Kumpati S Narendra and Kannan Parthasarathy · 1992
Earlier work this paper cites.
The proper orthogonal decomposition in the analysis of turbulent flows
Gal Berkooz, Philip Holmes, and John L Lumley · 1993
Earlier work this paper cites.
Application of feedforward neural networks to dynamical system identification and control
John G Kuschewski, Stefen Hui, and Stanislaw H Zak · 1993
Earlier work this paper cites.
Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems
S Elanayar Vt and Yung C Shin · 1994
Earlier work this paper cites.
Small‐scale structures in Boussinesq convection
Weinan E and Chi-wang Shu · 1994
Earlier work this paper cites.
Learning chaotic dynamics in recurrent rbf network
T Miyoshi, H Ichihashi, S Okamoto, and T Hayakawa · 1995
Earlier work this paper cites.
Phase-space learning
Fu-Sheng Tsung and Garrison W Cottrell · 1995
Earlier work this paper cites.
Time series prediction with multilayer perceptron, fir and elman neural networks
Timo Koskela, Mikko Lehtokangas, Jukka Saarinen, and Kimmo Kaski · 1996
Earlier work this paper cites.
Evolutionary algorithms that generate recurrent neural networks for learning chaos dynamics
Yuji Sato and Shigeki Nagaya · 1996
Earlier work this paper cites.
Dynamical system modeling via signal reduction and neural network simulation
Thomas L Paez and NF Hunter · 1997
Earlier work this paper cites.
Artificial neural network-based low-dimensional model for spatio-temporally varying cellular flames
Nejib Smaoui · 1997
Cited alongside, same era.
A recurrent neural network for modelling dynamical systems
Coryn AL Bailer-Jones, David JC MacKay, and Philip J Withers · 1998
Cited alongside, same era.
Characterization of nonlinear dynamic systems using artificial neural networks
Angel Urbina, Norman F Hunter, and Thomas L Paez · 1998
Cited alongside, same era.
Learning chaotic attractors by neural networks
Rembrandt Bakker, Jaap C Schouten, C Lee Giles, Floris Takens, and Cor M van den Bleek · 2000
Cited alongside, same era.
Nonlinear system modeling based on experimental data
Thomas L Paez and Norman F Hunter · 2000
Cited alongside, same era.
Time series analysis and its applications
Robert H Shumway and David S Stoffer · 2000
Large-scale video classification with convolutional neural networks
Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei · 2014
Later among the works it cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
Later among the works it cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Later among the works it cites.
Principal interval decomposition framework for pod reduced-order modeling of convective boussinesq flows
O San and J Borggaard · 2015
Later among the works it cites.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Gregory S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian J. Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Józefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Gordon Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul A. Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda B. Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
The elements of statistical learning
Jerome Friedman, Trevor Hastie, and Robert Tibshirani · 2001
Cited alongside, same era.
Strong stability-preserving high-order time discretization methods
Sigal Gottlieb, Chi-Wang Shu, and Eitan Tadmor · 2001
Cited alongside, same era.
A model for the unstable manifold of the bursting behavior in the 2d navier–stokes flow
Nejib Smaoui · 2001
Cited alongside, same era.
Long-term prediction of nonlinear hydrodynamics in bubble columns by using artificial neural networks
HY Lin, W Chen, and A Tsutsumi · 2003
Cited alongside, same era.
A fourth order scheme for incompressible boussinesq equations
Jian-Guo Liu, Cheng Wang, and Hans Johnston · 2003
Cited alongside, same era.
A hierarchy of low-dimensional models for the transient and post-transient cylinder wake
Bernd R Noack, Konstantin Afanasiev, MAREK MORZYŃSKI, Gilead Tadmor, and Frank Thiele · 2003
Cited alongside, same era.
Later among the works it cites.
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2016
Later among the works it cites.
Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Later among the works it cites.
A priori and a posteriori evaluations of sub-grid scale models for the burgers’ equation
Yanan Li and ZJ Wang · 2016
Later among the works it cites.
Inferring biological networks by sparse identification of nonlinear dynamics
Niall M Mangan, Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2016
Later among the works it cites.
Reduced order modeling of turbulent flows using statistical coarse-graining
Eric Parish and Karthikeyan Duraisamy · 2016
Later among the works it cites.
Synthesis of recurrent neural networks for dynamical system simulation
Adam P Trischler and Gabriele MT D’Eleuterio · 2016
Later among the works it cites.
Revise saturated activation functions
Bing Xu, Ruitong Huang, and Mu Li · 2016
Later among the works it cites.
Machine Learning Control-Taming Nonlinear Dynamics and Turbulence
Thomas Duriez, Steven L Brunton, and Bernd R Noack · 2017
Later among the works it cites.
Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V. Le · 2017
Later among the works it cites.
Data-driven control of nonlinear systems: An on-line direct approach
Marko Tanaskovic, Lorenzo Fagiano, Carlo Novara, and Manfred Morari · 2017
Later among the works it cites.
Learning chaotic dynamics using tensor recurrent neural networks
Rose Yu, Stephan Zheng, and Yan Liu · 2017
Later among the works it cites.
Data-driven discovery of closure models
Shaowu Pan and Karthik Duraisamy · 2018
Closest in time.
Model identification of reduced order fluid dynamics systems using deep learning
Z Wang, D Xiao, F Fang, R Govindan, CC Pain, and Y Guo · 2018
Closest in time.