Data-driven automated discovery of variational laws hidden in physical systems

Zhilong Huang, Yanping Tian, Chunjiang Li, Guang Lin, Lingling Wu, Yong Wang, Hanqing Jiang

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


The automated discovery of physical laws from discrete noisy data is significant for evaluating the response, stability, and reliability of dynamic systems. In contract to the existing work on the discovery of differential laws, this paper presents a data-driven method to discover the variational laws of physical systems. The effectiveness and robustness to measurement noise are demonstrated with five physical cases. Two features of variational laws, the compact form and holistic viewpoint, lead to two intrinsic advantages in the data-driven discovery of variational laws, namely, reduced data requirement and robustness to noise. The presented data-driven method can be applied to discover variational laws in real time for physical fields or more complicated social sciences, with or without prior knowledge.

Original languageEnglish (US)
Article number103871
JournalJournal of the Mechanics and Physics of Solids
StatePublished - Apr 2020

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering


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