  The simulation of driven dissipative quantum dynamics is often prohibitively
computation-intensive, especially when it is calculated for various shapes of
the driving field. We engineer a new feature space for representing the field
and demonstrate that a deep neural network can be trained to emulate these
dynamics by mapping this representation directly to the target observables. We
demonstrate that with this approach, the system response can be retrieved many
orders of magnitude faster. We verify the validity of our approach using the
example of finite transverse Ising model irradiated with few-cycle magnetic
pulses interacting with a Markovian environment. We show that our approach is
sufficiently generalizable and robust to reproduce responses to pulses outside
the training set.
