sindy: Run main SINDy function

In sindyr: Sparse Identification of Nonlinear Dynamics

Description

Estimates coefficients for set of ordinary differential equations governing system variables.

Arguments

xs	Matrix of raw data
dx	Matrix of main system variable dervatives; if NULL, it estimates with finite differences from xs
dt	Sample interval, if data continuously sampled; default = 1
Theta	Matrix of features; if not supplied, assumes polynomial features of order 3
lambda	Threshold to use for iterated least squares sparsification (Brunton et al.)
B.expected	The function will compute a goodness of fit if supplied with an expected coefficient matrix B; default = NULL
verbose	Verbose mode outputs Theta and dx values in their entirety; default = FALSE
fit.its	Number of iterations to conduct the least-square threshold sparsification; default = 10
plot.eq.graph	When set to TRUE, prints an igraph plot of variables as a graph structure; default = FALSE

Details

Uses the "left-division" approach of Brunton et al. (2016), and implements least-squares sparsification, and outputs coefficients after iterations stabilize.

Value

Returns a matrix B of coefficients specifying the relationship between dx and Theta

Author(s)

Rick Dale and Harish S. Bhat

References

Dale, R. and Bhat, H. S. (in press). Equations of mind: data science for inferring nonlinear dynamics of socio-cognitive systems. Cognitive Systems Research.

Brunton, S. L., Proctor, J. L., and Kutz, J. N. (2016). Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the National Academy of Sciences, 113(15), 3932-3937.

sindyr documentation built on July 8, 2020, 5:51 p.m.