SeFNO — Seismic Floor Acceleration Response Prediction (FNO v1.0+)
Pre-trained Fourier Neural Operator (FNO) models for predicting multi-floor acceleration response time histories of MDOF shear buildings subjected to seismic ground motions.
Code: github.com/HKUJasonJiang/Seismic-FNO
Task
Given a scaled ground motion acceleration time series (3 000 time steps, 50 Hz), the model predicts the roof-floor acceleration response of a target building — a pure regression task over 1-D signals.
| Input | Shape | Description |
|---|---|---|
| Ground motion | (1, 3000) |
Scaled accelerogram (m/s²) |
| Output | Shape | Description |
|---|---|---|
| Floor acceleration | (1, 3000) |
Roof acceleration response (m/s²) |
Available Models
Baseline Models
Three FNO configurations trained for 50 epochs on the full KNET dataset (3 474 GMs × 57 amplitude scale factors, 250 building configurations):
| Folder | Hidden (h) |
Modes (m) |
Layers (l) |
Parameters |
|---|---|---|---|---|
Base-FNO_v1.0+_h64_m64_l4_e50_*/ |
64 | 64 | 4 | ~3 M |
Large-FNO_v1.0+_h64_m512_l8_e50_*/ |
64 | 512 | 8 | ~12 M |
Huge-FNO_v1.0+_h128_m1024_l12_e50_*/ |
128 | 1024 | 12 | ~48 M |
Experimental Series
| Folder | Runs | Purpose |
|---|---|---|
Test-Series (Test-1~10)/ |
10 | Hyper-parameter sweep (modes, layers, hidden channels) |
Efficiency-Series (E-Base, E-Test-1~14)/ |
15 | Dataset-size ablation (varying number of GMs and scale factors) |
Each model folder contains:
<model_folder>/
├── model/
│ └── fno_best.pth # Best checkpoint (lowest validation loss)
└── details/
├── training_log.csv # Epoch-by-epoch MSE / RMSE / MAE / R²
├── training_config.txt # Full hyperparameter configuration
├── dataset_indices.pkl # Reproducible train / val / test split indices
└── test_results.txt # Final test-set metrics
Usage
Install dependencies
Refer to github repo.
Quick review notebook
Open quick_inference.ipynb in the cloned repository to run inference on the held-out test set and visualise time-history and Fourier amplitude spectrum comparisons interactively.