  A trained attention-based transformer network can robustly recover the
complex topologies given by the Richtmyer-Meshkoff instability from a sequence
of hydrodynamic features derived from radiographic images corrupted with blur,
scatter, and noise. This approach is demonstrated on ICF-like double shell
hydrodynamic simulations. The key component of this network is a transformer
encoder that acts on a sequence of features extracted from noisy radiographs.
This encoder includes numerous self-attention layers that act to learn temporal
dependencies in the input sequences and increase the expressiveness of the
model. This approach is demonstrated to exhibit an excellent ability to
accurately recover the Richtmyer-Meshkov instability growth rates, even despite
the gas-metal interface being greatly obscured by radiographic noise.
