Dimensionally Consistent Learning with Buckingham Pi. (arXiv:2202.04643v1 [cs.LG])

In the absence of governing equations, dimensional analysis is a robust
technique for extracting insights and finding symmetries in physical systems.
Given measurement variables and parameters, the Buckingham Pi theorem provides
a procedure for finding a set of dimensionless groups that spans the solution
space, although this set is not unique. We propose an automated approach using
the symmetric and self-similar structure of available measurement data to
discover the dimensionless groups that best collapse this data to a lower
dimensional …

arxiv learning pi