Data-driven discovery of dimensionless numbers and governing laws from scarce measurements.

ABSTRACT: Dimensionless numbers and scaling laws provide elegant insights into the characteristic properties of physical systems. Classical dimensional analysis and similitude theory fail to identify a set of unique dimensionless numbers for a highly multi-variable system with incomplete governing equations. This paper introduces a mechanistic data-driven approach that embeds the principle of dimensional invariance into a two-level machine learning scheme to automatically discover dominant dimensionless numbers and governing laws (including scaling laws and differential equations) from scarce measurement data. The proposed methodology, called dimensionless learning, is a physics-based dimension reduction technique. It can reduce high-dimensional parameter spaces to descriptions involving only a few

SUBMITTER: Xie X 

PROVIDER: S-EPMC9729234 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature