  Time history data collected from a Direct Current Potential Drop (DCPD)
fatigue experiment at a range of temperatures was used to train a Bidirectional
Long-Short Term Memory Neural Network (BiLSTM) model. The model was trained on
high sampling rate experimental data from crack initiation up through the Paris
regime. The BiLSTM model was able to predict the progressive crack extension at
intermediate temperatures and stress intensities. The model was able to
reproduce crack jumps and overall crack progression. The BiLSTM model
demonstrated the potential to be used as a tool for future investigation into
fundamental mechanisms such as high-temperature oxidation and new damage
models.
