Predicting streamflow with LSTM networks using global datasets

Streamflow predictions remain a challenge for poorly gauged and ungauged catchments. Recent research has shown that deep learning methods based on Long Short-Term Memory (LSTM) cells outperform process-based hydrological models for rainfall-runoff modeling, opening new possibilities for prediction in ungauged basins (PUB). These studies usually...

A hybrid approach to implement the Digital Twin concept into a damage evolution prediction for composite structures

Improvement in the SHM of composite materials requires an enhanced understanding of the damage accumulation processes and helps in the way towards lighter, more optimized, and more sustainable aerospace structures. The Digital Twin concept has the potential to address this problem and may revolutionize the designing, certifying, maintaining, and...