  The spin distribution of binary black hole mergers contains key information
concerning the formation channels of these objects, and the astrophysical
environments where they form, evolve and coalesce. To quantify the suitability
of deep learning to characterize the signal manifold of quasi-circular,
spinning, non-precessing binary black hole mergers, we introduce a modified
version of WaveNet trained with a novel optimization scheme that incorporates
general relativistic constraints of the spin properties of astrophysical black
holes. The neural network model is trained, validated and tested with 1.5
million $\ell=|m|=2$ waveforms generated within the regime of validity of
NRHybSur3dq8, i.e., mass-ratios $q\leq8$ and individual black hole spins $ |
s^z_{\{1,\,2\}} | \leq 0.8$. Using this neural network model, we quantify how
accurately we can infer the astrophysical parameters of black hole mergers in
the absence of noise. We do this by computing the overlap between waveforms in
the testing data set and the corresponding signals whose mass-ratio and
individual spins are predicted by our neural network. We find that the
convergence of high performance computing and physics-inspired optimization
algorithms enable an accurate reconstruction of the mass-ratio and individual
spins of binary black hole mergers across the parameter space under
consideration. This is a significant step towards an informed utilization of
physics-inspired deep learning models to reconstruct the spin distribution of
binary black hole mergers in realistic detection scenarios.
