Three-dimensional femtosecond laser beam shaping by real-time training and inference of a physics informed machine learning model
Bibliographic record
Abstract
A physics-informed machine learning (PIML) model has been trained and optimized for three-dimensional (3D) shaping of a focused ultrafast laser, with the incorporation of physical optics into model training under a 3F optical system. This PIML model enables an unprecedented range of tailoring in the 3D structural design of refractive index elements used in the fabrication of diffractive optics. The PIML model has been trained with physical optics-simulated data based on various orthonormal bases, such as Bessel and generalized Bessel beams. The objective here is to transfer the learning of the PIML model to correct 3D beam profiles at the focus, by training the PIML model with real-time experimental data captured with a 3D beam profiling and analysis system. The results of training with Bessel and generalized Bessel beams are presented for a 3F optical beam system. The resultant PIML model provided rapid, accurate, and real-time predictions for generating arbitrary 3D beam shapes in the lab, a capability otherwise impossible with traditional phase retrieval algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".