Long short-term memory (LSTM) neural networks for in situ particle velocity determination in material strength experiments under ramp wave compression

In the experiments of measuring the strength of materials under ramp compression, accurately determining in situ particle velocity is crucial for calculating material sound speed during loading–unloading path and materials strength under high pressure. This paper proposes a machine learning approach that utilizes Long Short-Term Memory (LSTM) neural networks and Bayesian optimization algorithms to enhance the analysis of data from ramp compression strength measurement experiments. This method leverages LSTM neural networks to uncover the complex relationship between the rear interface velocity of the sample and the in situ particle velocity in numerical simulations. By using a well-trained network model, it enables direct interpretation of experimental data, leading to accurate predictions of key physical quantities along the loading and unloading paths in ramp compression experiments. A comparative analysis between theoretical curves from numerical simulations and LSTM neural network predictions shows a high degree of consistency. This approach is applied to ramp compression experiments on Ta and CuCrZr materials, demonstrating superior accuracy over the free-surface approximation and incremental impedance matching methods. Additionally, this method relies solely on the equation of state during numerical computations, eliminating the need for the complex constitutive equations required by the transfer function method, thus enhancing data processing efficiency and practicality.