Fetching the paper…
Reading the bibliography…
Discovering dynamical models to describe underlying dynamical behavior is essential to draw decisive conclusions and engineering studies, e.g., optimizing a process.
Die kinetik der invertinwirkung
Leonor Michaelis and Maud L Menten · 1913
Earlier work this paper cites.
A further note on the kinetics of enzyme action
George Edward Briggs · 1925
Earlier work this paper cites.
Mathematical models of threshold phenomena in the nerve membrane
Richard FitzHugh · 1955
Earlier work this paper cites.
Deterministic nonperiodic flow
Edward N Lorenz · 1963
Earlier work this paper cites.
Smoothing and differentiation of data by simplified least squares procedures
Abraham Savitzky and Marcel JE Golay · 1964
Earlier work this paper cites.
Equations of motion from a data series
James P Crutchfield and Bruce S McNamara · 1987
Earlier work this paper cites.
Identification and control of dynamical systems using neural networks
S Narendra Kumpati and Parthasarathy Kannan · 1990
Earlier work this paper cites.
Artificial Neural Networks for Modelling and Control of Non-Linear Systems
Johan AK Suykens, Joos PL Vandewalle, and Bart L de Moor · 1996
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
Earlier work this paper cites.
Subspace Identification of Linear Systems: Theory, Implementation, Applications
Peter Van Overschee and Bart de Moor · 1996
Earlier work this paper cites.
Computer methods for ordinary differential equations and differential-algebraic equations
Uri M Ascher and Linda R Petzold · 1998
Earlier work this paper cites.
System Identification: Theory for the User
Lennart Ljung · 1999
Earlier work this paper cites.
The elements of statistical learning
Jerome Friedman, Trevor Hastie, Robert Tibshirani, et al · 2001
Earlier work this paper cites.
Equation-free, coarse-grained multiscale computation: Enabling mocroscopic simulators to perform system-level analysis
Ioannis G Kevrekidis, C William Gear, James M Hyman, Panagiotis G Kevrekidis, Olof Runborg, Constantinos Theodoropoulos, et al · 2003
Earlier work this paper cites.
Nonlinear Time Series Analysis
Holger Kantz and Thomas Schreiber · 2004
Earlier work this paper cites.
Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information
Emmanuel J Candès, Justin Romberg, and Terence Tao · 2006
Earlier work this paper cites.
Stable signal recovery from incomplete and inaccurate measurements
Emmanuel J Candès, Justin K Romberg, and Terence Tao · 2006
Cited alongside, same era.
Compressed sensing
David L Donoho · 2006
Cited alongside, same era.
Automated reverse engineering of nonlinear dynamical systems
Josh Bongard and Hod Lipson · 2007
Cited alongside, same era.
Signal recovery from random measurements via orthogonal matching pursuit
Joel A Tropp and Anna C Gilbert · 2007
Cited alongside, same era.
Distilling free-form natural laws from experimental data
Michael Schmidt and Hod Lipson · 2009
Cited alongside, same era.
Numerical differentiation of noisy, nonsmooth data
Rick Chartrand · 2011
Cited alongside, same era.
Machine learning: Trends, perspectives, and prospects
Michael I Jordan and Tom M Mitchell · 2015
Later among the works it cites.
Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling
Hao Ye, Richard J Beamish, Sarah M Glaser, Sue CH Grant, Chih-hao Hsieh, Laura J Richards, Jon T Schnute, and George Sugihara · 2015
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.
Sparse identification of nonlinear dynamics with control (SINDYc)
Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2016
Later among the works it cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Later among the works it cites.
Inferring biological networks by sparse identification of nonlinear dynamics
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The original Michaelis constant: translation of the 1913 Michaelis–Menten paper
Kenneth A Johnson and Roger S Goody · 2011
Cited alongside, same era.
Automated refinement and inference of analytical models for metabolic networks
Michael D Schmidt, Ravishankar R Vallabhajosyula, Jerry W Jenkins, Jonathan E Hood, Abhishek S Soni, John P Wikswo, and Hod Lipson · 2011
Cited alongside, same era.
Predicting catastrophes in nonlinear dynamical systems by compressive sensing
Wen-Xu Wang, Rui Yang, Ying-Cheng Lai, Vassilios Kovanis, and Celso Grebogi · 2011
Cited alongside, same era.
Sparsity constrained nonlinear optimization: Optimality conditions and algorithms
Amir Beck and Yonina C Eldar · 2013
Cited alongside, same era.
An introduction to statistical learning
Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani · 2013
Cited alongside, same era.
The big challenges of big data
Vivien Marx · 2013
Cited alongside, same era.
Niall M Mangan, Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2016
Later among the works it cites.
Sparse nonlinear regression: Parameter estimation under nonconvexity
Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina Eldar, and Tong Zhang · 2016
Later among the works it cites.
Optimal shrinkage of singular values
Matan Gavish and David L Donoho · 2017
Later among the works it cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Later among the works it cites.
Multistep neural networks for data-driven discovery of nonlinear dynamical systems
Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2018
Later among the works it cites.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
Later among the works it cites.
Deep learning of dynamics and signal-noise decomposition with time-stepping constraints
Samuel H Rudy, J Nathan Kutz, and Steven L Brunton · 2019
Later among the works it cites.
A unified sparse optimization framework to learn parsimonious physics-informed models from data
Kathleen Champion, Peng Zheng, Aleksandr Y Aravkin, Steven L Brunton, and J Nathan Kutz · 2020
Later among the works it cites.
PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data
Brian de Silva, Kathleen Champion, Markus Quade, Jean-Christophe Loiseau, J. Kutz, and Steven Brunton · 2020
Later among the works it cites.
The imperative of physics-based modeling and inverse theory in computational science
Karen E Willcox, Omar Ghattas, and Patrick Heimbach · 2021
Closest in time.