  We present a deep-learning artificial intelligence model that is capable of
learning and forecasting the late-inspiral, merger and ringdown of numerical
relativity waveforms that describe quasi-circular, spinning, non-precessing
binary black hole mergers. We used the NRHybSur3dq8 surrogate model to produce
train, validation and test sets of $\ell=|m|=2$ waveforms that cover the
parameter space of binary black hole mergers with mass-ratios $q\leq8$ and
individual spins $|s^z_{\{1,2\}}| \leq 0.8$. These waveforms cover the time
range $t\in[-5000\textrm{M}, 130\textrm{M}]$, where $t=0M$ marks the merger
event, defined as the maximum value of the waveform amplitude. We harnessed the
ThetaGPU supercomputer at the Argonne Leadership Computing Facility to train
our AI model using a training set of 1.5 million waveforms. We used 16 NVIDIA
DGX A100 nodes, each consisting of 8 NVIDIA A100 Tensor Core GPUs and 2 AMD
Rome CPUs, to fully train our model within 3.5 hours. Our findings show that
artificial intelligence can accurately forecast the dynamical evolution of
numerical relativity waveforms in the time range $t\in[-100\textrm{M},
130\textrm{M}]$. Sampling a test set of 190,000 waveforms, we find that the
average overlap between target and predicted waveforms is $\gtrsim99\%$ over
the entire parameter space under consideration. We also combined scientific
visualization and accelerated computing to identify what components of our
model take in knowledge from the early and late-time waveform evolution to
accurately forecast the latter part of numerical relativity waveforms. This
work aims to accelerate the creation of scalable, computationally efficient and
interpretable artificial intelligence models for gravitational wave
astrophysics.
