Public
Acoustic Emission Testing (AE) — Public Tier
(5 scenes)Full-access development tier with all data visible.
What you get
Measurements (y), ideal forward operator (H), spec ranges, ground truth (x_true), and true mismatch spec.
How to use
Load HDF5 → compare reconstruction vs x_true → check consistency → iterate.
What to submit
Reconstructed signals (x_hat) and corrected spec as HDF5.
Parameter Specifications
✓
True spec visible — use these exact values for Scenario III oracle reconstruction.
| Parameter | Spec Range | True Value | Unit |
|---|---|---|---|
| source_location_error | -1.0 – 2.0 | 0.5 | mm |
| wave_speed_error | 5860.0 – 5980.0 | 5920.0 | m/s |
| sensor_coupling_gain | 0.96 – 1.08 | 1.02 | - |
| arrival_time_bias | -0.1 – 0.2 | 0.05 | us |
InverseNet Baseline Scores
Method: CPU_baseline — Mismatch parameter: nominal
Scenario I (Ideal)
7.77 dB
SSIM 0.3772
Scenario II (Mismatch)
7.61 dB
SSIM 0.1940
Scenario III (Oracle)
15.93 dB
SSIM 0.4222
Per-scene breakdown (4 scenes)
| Scene | PSNR I | SSIM I | PSNR II | SSIM II | PSNR III | SSIM III |
|---|---|---|---|---|---|---|
| scene_00 | 7.57 | 0.3615 | 7.48 | 0.1918 | 15.88 | 0.4337 |
| scene_01 | 7.70 | 0.3833 | 7.85 | 0.1983 | 15.89 | 0.4210 |
| scene_02 | 7.97 | 0.3846 | 7.45 | 0.1929 | 16.02 | 0.4175 |
| scene_03 | 7.84 | 0.3794 | 7.67 | 0.1931 | 15.94 | 0.4166 |
Public Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | SwinIR-AE + gradient | 0.803 | 33.33 | 0.955 | 0.94 | ✓ Certified | Liang et al., ICCV 2021; AE-adapted 2024 |
| 2 | DiffusionAE + gradient | 0.790 | 32.9 | 0.952 | 0.9 | ✓ Certified | Song et al., ICLR 2021; SHM application 2024 |
| 3 | PINN-AE + gradient | 0.760 | 30.72 | 0.927 | 0.91 | ✓ Certified | Raissi et al., J. Comput. Phys. 2019; AE extension 2024 |
| 4 | Domain-Adapted ResNet + gradient | 0.736 | 29.34 | 0.906 | 0.9 | ✓ Certified | Tabian et al., Sensors 2019 |
| 5 | AE-CNN + gradient | 0.698 | 27.16 | 0.862 | 0.91 | ✓ Certified | Ebrahimkhanlou & Salamone, Struct. Health Monit. 2019 |
| 6 | PnP-ADMM + gradient | 0.678 | 25.77 | 0.825 | 0.95 | ✓ Certified | Venkatakrishnan et al., IEEE GlobalSIP 2013 |
| 7 | Sparse TR (L1) + gradient | 0.597 | 22.72 | 0.72 | 0.91 | ✓ Certified | Gao et al., J. Sound Vib. 2016 |
| 8 | TDOA-WLS + gradient | 0.544 | 20.65 | 0.629 | 0.93 | ✓ Certified | Kundu, J. Acoust. Soc. Am. 2014 |
| 9 | Time-Reversal Imaging + gradient | 0.494 | 18.88 | 0.544 | 0.94 | ✓ Certified | Fink, IEEE UFFC 1992; applied to AE: Grosse & Ohtsu 2008 |
Visible Data Fields
y
H_ideal
spec_ranges
x_true
true_spec
Dataset
Format: HDF5
Scenes: 5
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%