Robust Low-Rank Discovery of Data-Driven Partial Differential Equations



Published on


Partial differential equations (PDEs) are essential foundations to model dynamic processes in natural sciences. Discovering the underlying PDEs of complex data collected from real world is key to understanding the dynamic processes of natural laws or behaviors. However, both the collected data and their partial derivatives are often corrupted by noise, especially from sparse outlying entries, due to measurement/process noise in the real-world applications. Our work is motivated by the observation that the underlying data modeled by PDEs are in fact often low rank. We thus develop a robust low-rank discovery framework to recover both the low-rank data and the sparse outlying entries by integrating double low-rank and sparse recoveries with a (group) sparse regression method, which is implemented as a minimization problem using mixed nuclear norms with 1 and 0 norms. We propose a low-rank sequential (grouped) threshold ridge regression algorithm to solve the minimization problem. Results from several experiments on seven canonical models (i.e., four PDEs and three parametric PDEs) verify that our framework outperforms the state-of-art sparse and group sparse regression methods.

Please cite our work using the BibTeX below.

title={Robust Low-Rank Discovery of Data-Driven Partial Differential Equations}, 
journal={Proceedings of the AAAI Conference on Artificial Intelligence}, 
author={Li, Jun and Sun, Gan and Zhao, Guoshuai and Lehman, Li-wei H.}, 
Close Modal