Abstract
We develop an algorithm for model selection which allows for the consideration of a combinatorially large number of candidate models governing a dynamical system. The innovation circumvents a disadvantage of standard model selection which typically limits the number of candidate models considered due to the intractability of computing information criteria. Using a recently developed sparse identification of nonlinear dynamics algorithm, the sub-selection of candidate models near the Pareto frontier allows feasible computation of Akaike information criteria (AIC) or Bayes information criteria scores for the remaining candidate models. The information criteria hierarchically ranks the most informative models, enabling the automatic and principled selection of the model with the strongest support in relation to the time-series data. Specifically, we show that AIC scores place each candidate model in the strong support, weak support or no support category. The method correctly recovers several canonical dynamical systems, including a susceptible-exposedinfectious-recovered disease model, Burgers' equation and the Lorenz equations, identifying the correct dynamical system as the only candidate model with strong support.
Original language | English (US) |
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Article number | 20170009 |
Journal | Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences |
Volume | 473 |
Issue number | 2204 |
DOIs | |
State | Published - Aug 1 2017 |
Funding
Data accessibility. This paper contains no experimental data. All computational results are reproducible and code is available at: https://github.com/niallmm/SINDy_AIC. Authors’ contributions. All authors conceived of the work, designed the study and drafted the manuscript. N.M.M. carried out the computations. Competing interests. We declare we have no competing interests. Funding. J.N.K. acknowledges support from the Air Force Office of Scientific Research (FA9550-15-1-0385). S.L.B. acknowledges support from the Air Force Office of Scientific Research (FA-9550-16-1-0650). S.L.B. and J.N.K. acknowledge support from the Defense Advanced Research Projects Agency (DARPA contract HR0011-16-C-0016). J.L.P. and N.M.M. thank Bill and Melinda Gates for their active support of the Institute for Disease Modeling and their sponsorship through the Global Good Fund.
Keywords
- Data-driven discovery
- Information criteria
- Model selection
- Nonlinear dynamics
- Sparse regression
ASJC Scopus subject areas
- General Engineering
- General Physics and Astronomy
- General Mathematics