Deep Learning‐Based Prediction of Controllable Stress/Strain‐Rate Loading and Design for Loading Conditions

ABSTRACT The graded density impactor (GDI) dynamic loading technique serves as a crucial method for achieving controllable stress/strain‐rate loading, where the loading velocity and adaptability of GDI structural design critically govern the loading results. Numerical simulations of layer‐wise modulation and shock wave transmission revealed a decoupling mechanism for stress and strain‐rate parameters. Specifically, the loading velocity determines the overall magnitude, whereas variations in interlayer thickness modulate the specific strain‐rate loading path. Building on this, a branched convolutional neural network (CNN)‐bidirectional long short‐term memory model (BLSTM) is developed to simultaneously predict stress/strain‐rate curves achieving R 2 = 0.95 and loading velocity achieving R 2 = 0.99 while enabling GDI thickness design. This methodology resolves multi‐physics coupling challenges in curve prediction and offers solutions for time‐dependent issues in extreme conditions.