Enhancing the extrapolative capabilities of deep learning models on temporal parameter sets in femtosecond laser processing

Deep learning has emerged as a powerful tool for modeling multi-scale phenomena in laser processing. However, a major challenge remains in achieving reliable extrapolation beyond the range of training data. In this presentation, we propose a neural network architecture designed to improve extrapolative performance for time-domain parameter sets in femtosecond laser processing.