# Machine Discovery of Partial Differential Equations from Spatiotemporal Data

@article{Yuan2019MachineDO, title={Machine Discovery of Partial Differential Equations from Spatiotemporal Data}, author={Ye Yuan and Junlin Li and Liang Li and Frank Jiang and Xiuchuan Tang and Fumin Zhang and Sheng Liu and Jorge M. Gonçalves and Henning U. Voss and Xiuting Li and J{\"u}rgen Kurths and Han Ding}, journal={ArXiv}, year={2019}, volume={abs/1909.06730} }

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery ($\text{S}^3\text{d}$), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning…

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