Reconstructing Richtmyer-Meshkov instabilities from noisy radiographs using low dimensional features and attention-based neural networks
This work addresses a specific challenge in inertial confinement fusion (ICF) diagnostics, offering a robust method for analyzing hydrodynamic instabilities from noisy data.
The paper tackled the problem of reconstructing Richtmyer-Meshkov instabilities from noisy radiographs by using an attention-based transformer network, achieving excellent recovery of growth rates even with obscured interfaces.
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.