Richtmyer Meshkov Instability

The Richtmyer-Meshkov instability (RMI) describes the turbulent mixing that occurs when a shock wave interacts with a perturbed interface between two fluids. Current research focuses on improving the accuracy and robustness of RMI simulations, particularly using deep learning models, such as attention-based neural networks, to reconstruct complex flow patterns from noisy experimental data. Researchers are also exploring the use of conservation laws to assess the accuracy of these models and improve their predictive capabilities, aiming for better understanding and prediction of RMI in applications like inertial confinement fusion.

Papers

August 2, 2024

Reconstructing Richtmyer-Meshkov instabilities from noisy radiographs using low dimensional features and attention-based neural networks
Daniel A. Serino, Marc L. Klasky, Balasubramanya T. Nadiga, Xiaojian Xu, Trevor Wilcox
Transformer Encoder Dimensional Feature Attention Based Transformer Attention Based Neural Network Hydrodynamic Simulation Noisy MRI Richtmyer Meshkov Instability

July 19, 2022

Using Conservation Laws to Infer Deep Learning Model Accuracy of Richtmyer-meshkov Instabilities
Charles F. Jekel, Dane M. Sterbentz, Sylvie Aubry, Youngsoo Choi, Daniel A. White, Jonathan L. Belof
Deep Learning Conservation Law Deep Learning Inference Wave Equation Hydrodynamic Simulation Impact Mechanic Richtmyer Meshkov Instability

Richtmyer Meshkov Instability

Papers

Reconstructing Richtmyer-Meshkov instabilities from noisy radiographs using low dimensional features and attention-based neural networks

Using Conservation Laws to Infer Deep Learning Model Accuracy of Richtmyer-meshkov Instabilities