Weak SINDy For Partial Differential Equations

• URL: http://arxiv.org/abs/2007.02848v3
• Date: Mon, 21 Dec 2020 17:26:50 GMT
• Title: Weak SINDy For Partial Differential Equations
• Authors: Daniel A. Messenger and David M. Bortz
• Abstract summary: We extend our Weak SINDy (WSINDy) framework to the setting of partial differential equations (PDEs) The elimination of pointwise derivative approximations via the weak form enables effective machine-precision recovery of model coefficients from noise-free data. We demonstrate WSINDy's robustness, speed and accuracy on several challenging PDEs.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparse Identification of Nonlinear Dynamics (SINDy) is a method of system discovery that has been shown to successfully recover governing dynamical systems from data (Brunton et al., PNAS, '16; Rudy et al., Sci. Adv. '17). Recently, several groups have independently discovered that the weak formulation provides orders of magnitude better robustness to noise. Here we extend our Weak SINDy (WSINDy) framework introduced in (arXiv:2005.04339) to the setting of partial differential equations (PDEs). The elimination of pointwise derivative approximations via the weak form enables effective machine-precision recovery of model coefficients from noise-free data (i.e. below the tolerance of the simulation scheme) as well as robust identification of PDEs in the large noise regime (with signal-to-noise ratio approaching one in many well-known cases). This is accomplished by discretizing a convolutional weak form of the PDE and exploiting separability of test functions for efficient model identification using the Fast Fourier Transform. The resulting WSINDy algorithm for PDEs has a worst-case computational complexity of $\mathcal{O}(N^{D+1}\log(N))$ for datasets with $N$ points in each of $D+1$ dimensions (i.e. $\mathcal{O}(\log(N))$ operations per datapoint). Furthermore, our Fourier-based implementation reveals a connection between robustness to noise and the spectra of test functions, which we utilize in an \textit{a priori} selection algorithm for test functions. Finally, we introduce a learning algorithm for the threshold in sequential-thresholding least-squares (STLS) that enables model identification from large libraries, and we utilize scale-invariance at the continuum level to identify PDEs from poorly-scaled datasets. We demonstrate WSINDy's robustness, speed and accuracy on several challenging PDEs.

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