  We introduce a machine-learning approach (denoted Symmetry Seeker Neural
Network) capable of automatically discovering discrete symmetry groups in
physical systems. This method identifies the finite set of parameter
transformations that preserve the system's physical properties. Remarkably, the
method accomplishes this without prior knowledge of the system's symmetry or
the mathematical relationships between parameters and properties. Demonstrating
its versatility, we showcase examples from mathematics, nanophotonics, and
quantum chemistry.
