Machine learning-based modeling for structural modification induced by femtosecond laser pulses
Abstract
We present our work on modeling structural changes induced by ultrashort pulse lasers using deep learning. Effective deep learning-based modeling requires the collection of high-quality processing data in large quantities. To achieve this, we developed an autonomous data acquisition system. By integrating an ultrashort pulse laser irradiation system, we established an experimental setup that automatically conducts processing and measurement experiments across various laser parameters while providing parameter feedback based on measurement results.
Fields of Study & Subjects
Laser Material Processing Techniques
Laser-Matter Interactions and Applications
Machine Learning in Materials Science
Publication details
- Journal / source
- Nanoscale and Quantum Materials: From Synthesis and Laser Processing to Applications
- Pages
- 12–12
- Publication date
- 4 March 2026
- Language
- EN