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lambda1 float64 0.51 1.23 | lambda2 float64 0.79 1.48 | blur_radius int64 0 4 | optimal_design array 2D | optimization_history listlengths 201 201 | total_overlap float64 -0.03 8.64 |
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1.189657 | 1.435928 | 0 | [[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.(...TRUNCATED) | [0.3429715931415558,0.328496515750885,1.2297134399414062,2.8662593364715576,3.027003288269043,3.8130(...TRUNCATED) | 5.741875 |
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Photonics 2D (120x120) - Wavelength Demultiplexer (v1)
Inverse-design dataset for the EngiBench photonics2d problem: 2D photonic wavelength
demultiplexers optimized with a finite-difference frequency-domain (FDFD) solver (ceviche).
Each design routes light at two wavelengths to two separate output ports.
v1
v1 makes simulate and optimize mutually consistent: designs are stored as physical (projected)
densities in [0, 1], and simulate evaluates them as-is, so the stored total_overlap is
reproducible via Photonics2D.simulate(optimal_design). Boundary conditions match v0 one-for-one.
Columns
| Column | Description |
|---|---|
lambda1, lambda2 |
input wavelengths (um) |
blur_radius |
density blur-filter radius (px) |
optimal_design |
120x120 density in [0, 1] |
optimization_history |
objective (total_overlap) per optimization step |
total_overlap |
final objective (higher is better) |
Splits: train / val / test = 1594 / 199 / 200.
Usage
from datasets import load_dataset
ds = load_dataset("IDEALLab/photonics_2d_120_120_v1")
Generated with EngiBench.
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