On-the-fly training architecture for a time-series neural network on ADITYA/ADITYA-U data

Abstract

Plasma disruption prediction using the data drive technique is popular for fusion experiments. Auto-encoder LSTM neural network is implemented on ADITYA/ADITYA-U data to predict the plasma disruption. There is a generic way of processing time series from a dataset that facilitates efficient generalization on new time-series data using a transfer learning technique. This technique enables task-specific parameter improvement with residual correction on univariate time series. This provides an on-the-fly architecture to expand the training model for new data without retraining the whole model. It also provides layer updates based on linearization learning for new input data This architecture enables us to update real-time saved models with new features of data that provide performance improvement during prediction. The demo is tested on ADITYA/ADITYA-U data which save time and run time updates to improve the accuracy of data. The improved results are derived by following the trend while predicting the result after adding new features via this architecture to the existing saved model. This technique allows automated on-the-fly training architecture at time-to-time basis or explicit need of retraining with new data.