RT Journal Article
SR Electronic
T1 Data-driven discovery of partial differential equations
JF Science Advances
JO Sci Adv
FD American Association for the Advancement of Science
SP e1602614
DO 10.1126/sciadv.1602614
VO 3
IS 4
A1 Rudy, Samuel H.
A1 Brunton, Steven L.
A1 Proctor, Joshua L.
A1 Kutz, J. Nathan
YR 2017
UL http://advances.sciencemag.org/content/3/4/e1602614.abstract
AB We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Kortewegâ€“de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable.