IDAS Semiar 2.10.2020: Extracting differential equations from data with SEED 2.0

by Rui Carvalho (Dept of Engineering (Durham) & IDAS)

Friday 2 Oct 2020, 16:00 → 17:00 Europe/London

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We have developed SEED 2.0 (Software for the Extraction of Equations from Data) in Python to bridge the gap between existing mathematical software

libraries to extract differential equations from data and the community of scientists who may be unaware of the new algorithms.

SEED 2.0 is available on GitHub at https://github.com/Statistical-Learning-4-System-Id/.  SEED allows users to extract differential equations from data by combining the SINDy algorithm [1-2] with a GUI written in Python. Users can select the system under analysis, set the parameters for ODEs and PDEs, as well as visualise model output plots.

 

We will introduce the SINDy algorithm, outline the route towards SEED 3.0 (expected Summer 2021) and discuss open problems in the automatic extraction of differential equations from data. 

[1] Brunton, S. L., Proctor, J. L. & Kutz, J. N. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc. Natl. Acad. Sci. U. S. A. 113, 3932-3937,
doi:10.1073/pnas.1517384113 (2016).

 

[2] de Silva et al., (2020). PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data. Journal of Open Source Software, 5(49), 2104, https://doi.org/10.21105/joss.02104

[3] Rudy, S. H., Brunton, S. L., Proctor, J. L. & Kutz, J. N.
Data-driven discovery of partial differential equations. Sci. Adv. 3, 6,
doi:10.1126/sciadv.1602614 (2017).