  The waveform templates of the matched filtering-based gravitational-wave
search ought to cover wide range of parameters for the prosperous detection.
Numerical relativity (NR) has been widely accepted as the most accurate method
for modeling the waveforms. Still, it is well-known that NR typically requires
a tremendous amount of computational costs. In this paper, we demonstrate a
proof-of-concept of a novel deterministic deep learning (DL) architecture that
can generate gravitational waveforms from the merger and ringdown phases of the
non-spinning binary black hole coalescence. Our model takes ${\cal O}$(1)
seconds for generating approximately $1500$ waveforms with a 99.9\% match on
average to one of the state-of-the-art waveform approximants, the
effective-one-body. We also perform matched filtering with the DL-waveforms and
find that the waveforms can recover the event time of the injected
gravitational-wave signals.
