| --- |
| library_name: pytorch |
| tags: |
| - pytorch |
| - photonics |
| - electromagnetics |
| - surrogate-model |
| - scientific-computing |
| --- |
| |
| # Neon Slab Predictor |
|
|
| A fast neural network predictor for dielectric slab optical response, trained on FDFD simulation data from the Neon solver. |
|
|
| ## What it does |
| Give it a dielectric slab configuration. Get optical response back in milliseconds. |
|
|
| Inputs: slab thickness (um), relative permittivity real part, wavelength (um) |
| Outputs: normalized transmission, normalized reflection, normalized peak intensity |
|
|
| ## Install and use |
| ```python |
| from neon import Neon |
| model = Neon.from_pretrained() |
| result = model.predict( |
| thickness=0.30, epsilon_real=2.25, wavelength=0.80 |
| ) |
| print(result) |
| ``` |
|
|
| ## What this is |
| This model is trained on 2D scalar FDFD simulation data from the Neon benchmark solver. It covers one geometry class: a rectangular dielectric slab at normal incidence in vacuum. Within that class it is a fast, usable predictor. |
|
|
| ## What this is not |
| This model does not generalize to metasurfaces, waveguides, photonic crystals, multilayer stacks, oblique incidence, dispersive materials, or full-vector Maxwell problems. If your structure is not a simple dielectric slab, this model will give you a number that means nothing. |
|
|
| ## Performance |
| Saved single-model test MAE: |
| - transmission: 0.053586 |
| - reflection: 0.055240 |
| - intensity: 0.234785 |
|
|
| Saved OOD degradation warning: |
| - benchmark-facing Model C ensemble OOD transmission MAE: 0.107476 |
| - benchmark-facing Model C ensemble OOD reflection MAE: 0.105841 |
| - benchmark-facing Model C ensemble OOD intensity MAE: 0.174486 |
| - overall OOD mean MAE: 0.129268 |
|
|
| Note: the repository does not currently store a single-model benchmark-facing OOD summary. The OOD warning above comes from the saved 5-member Model C ensemble evaluation. |
|
|
| ## Training data range |
| - thickness: 0.12 to 0.46 um |
| - epsilon_real: 1.4 to 4.0 |
| - wavelength: 0.72 to 0.92 um |
| - inputs outside this range trigger validation; `predict()` raises a validation error by default and can warn instead with `warn_only=True` |
|
|
| ## Companion paper |
| Toward Trustworthy Surrogate Models for Electromagnetic Simulation: A Systematic Evaluation of Physics-Informed Training, Uncertainty, and Active Learning on a Controlled Benchmark |
|
|
| in preparation |
|
|
| ## Citation |
| TBD |
|
|
