  Neural networks are a prominent tool for identifying and modeling complex
patterns, which are otherwise hard to detect and analyze. While machine
learning and neural networks have been finding applications across many areas
of science and technology, their use in decoding ultrafast dynamics of quantum
systems driven by strong laser fields has been limited so far. Here we use deep
neural networks to analyze simulated noisy spectra of highly nonlinear optical
response of a 2-dimensional gapped graphene crystal to intense few-cycle laser
pulses. We show that a computationally simple 1-dimensional system provides a
useful "nursery school" for our neural network, allowing it to be easily
retrained to treat more complex systems, recovering the band structure and
spectral phases of the incident few-cycle pulse with high accuracy, in spite of
significant amplitude noise and phase jitter. Our results both offer a new tool
for attosecond spectroscopy of quantum dynamics in solids and also open a route
to developing all-solid-state devices for complete characterization of
few-cycle pulses, including their nonlinear chirp and the carrier envelope
phase.
