Fetching the paper…
Reading the bibliography…
We present a weak formulation and discretization of the system discovery problem from noisy measurement data.
A new look at the statistical model identification
H. Akaike · 1974
Earlier work this paper cites.
On entropy maximization principle
Hirotugu Akaike · 1977
Earlier work this paper cites.
Identification of significant host factors for HIV dynamics modelled by non-linear mixed-effects models
Hulin Wu and Lang Wu · 2002
Earlier work this paper cites.
Model Selection and Mixed-Effects Modeling of HIV Infection Dynamics
D. M. Bortz and P. W. Nelson · 2006
Earlier work this paper cites.
Numerical Methods in Scientific Computing: Volume 1
Germund Dahlquist and Ake Bjorck · 2008
Earlier work this paper cites.
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
Tina Toni, David Welch, Natalja Strelkowa, Andreas Ipsen, and Michael P.H Stumpf · 2009
Earlier work this paper cites.
Parameter Estimation and Model Selection in Computational Biology
Gabriele Lillacci and Mustafa Khammash · 2010
Earlier work this paper cites.
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.
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2016
Cited alongside, same era.
Machine learning of linear differential equations using Gaussian processes
Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
Cited alongside, same era.
Sparse model selection via integral terms
Hayden Schaeffer and Scott G McCalla · 2017
Cited alongside, same era.
Robust data-driven discovery of governing physical laws with error bars
Sheng Zhang and Guang Lin · 2018
Cited alongside, same era.
Ident: Identifying differential equations with numerical time evolution
Discovery of dynamics using linear multistep methods
Rachel Keller and Qiang Du · 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.
Using Experimental Data and Information Criteria to Guide Model Selection for Reaction–Diffusion Problems in Mathematical Biology
David J. Warne, Ruth E. Baker, and Matthew J. Simpson · 2019
Later among the works it cites.
On the convergence of the SINDy algorithm
Linan Zhang and Hayden Schaeffer · 2019
Later among the works it cites.
Sheng Zhang and Guang Lin · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Sung Ha Kang, Wenjing Liao, and Yingjie Liu · 2019
Cited alongside, same era.
Learning partial differential equations for biological transport models from noisy spatio-temporal data
John H. Lagergren, John T. Nardini, G. Michael Lavigne, Erica M. Rutter, and Kevin B. Flores · 2020
Closest in time.