  Gravitational waves, detected a century after they were first theorized, are
spacetime distortions caused by some of the most cataclysmic events in the
universe, including black hole mergers and supernovae. The successful detection
of these waves has been made possible by ingenious detectors designed by human
experts. Beyond these successful designs, the vast space of experimental
configurations remains largely unexplored, offering an exciting territory
potentially rich in innovative and unconventional detection strategies. Here,
we demonstrate the application of artificial intelligence (AI) to
systematically explore this enormous space, revealing novel topologies for
gravitational wave (GW) detectors that outperform current next-generation
designs under realistic experimental constraints. Our results span a broad
range of astrophysical targets, such as black hole and neutron star mergers,
supernovae, and primordial GW sources. Moreover, we are able to conceptualize
the initially unorthodox discovered designs, emphasizing the potential of using
AI algorithms not only in discovering but also in understanding these novel
topologies. We've assembled more than 50 superior solutions in a publicly
available Gravitational Wave Detector Zoo which could lead to many new
surprising techniques. At a bigger picture, our approach is not limited to
gravitational wave detectors and can be extended to AI-driven design of
experiments across diverse domains of fundamental physics.
