Predictive deep learning models for analyzing discrete fractional dynamics from noisy and incomplete data

We study the accuracy of machine learning methods for inferring the parameters of noisy fractional Wu–Baleanu trajectories with some missing initial terms. Our model is based on a combination of convolutional and recurrent neural networks (LSTM), which permits the extraction of characteristics from trajectories while preserving time dependency. We show that these approach exhibit good accuracy results despite the poor quality of the data. Summarizing:

• We combine convolutional neural networks and LSTM units.
• We predict the parameters from noisy trajectories that may have lost some terms.
• The errors in the predictions are of the same error magnitude as the noise.

Ò. Garibo-i-Orts et al. Predictive deep learning models for analyzing discrete fractional dynamics from noisy and incomplete data.» Chinese J. Phys. 89 (2024): 1276-1285. DOI:10.1016/j.cjph.2024.04.010