Predicting laser energy absorption on nanostructured surfaces with deep learning
Bibliographic record
Abstract
Brief laser pulses can induce autonomous organization of nanostructures pattern without external guidance. This interaction between a laser light and a material is governed by Maxwell's equations. These equations provide the theoretical framework for understanding how electromagnetic waves propagate and interact with matter. The Finite-Difference Time-Domain (FDTD) method models the laser-material interactions, providing insights into absorption, reflection, and scattering over time, ultimately contributing to self-organization within the material. Despite a theoretical understanding, there is no reliable model to predict the self-organization process responsible for the nanostructures. Our work addresses this issue by aiming to predict the surface changes after multiple laser irradiations using neural networks. Deep learning models have undergone advancements and prove suitable for extracting meaningful insights and simulating physical processes. This combination of laser physics and deep learning offer a promising approach to improve our ability to control nanostructures formation on materials.
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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) | 0.001 | 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".