Furthermore, we presented a method for adapting a symbolic function learner to find
mathematical relationships between measured quantities in an unsupervised way. This
method does not explicitly label any variate as being an output value, bypassing the
assumptions made in standard regression problems about causal relationships. We demonstrated how this method was able to recover rules of geometry from raw data alone. This included the law of sines and the Pythagorean theorem, two relationships on measurements relating to triangles where no variable is necessarily considered an output of a function of
the others.
Panju, Automated Knowledge Discovery
Using Neural Networks (2021)