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---
title: DeepONet FPO Demo
colorFrom: green
colorTo: green
sdk: gradio
sdk_version: 6.1.0
app_file: app.py
pinned: false
license: mit
short_description: 'Demo of unsteady flow around varied geometries'
---
# DeepONet FPO Demo (FlowBench)
This Space runs **time-dependent DeepONet** checkpoints (s ∈ {1,4,8,16}) to generate **auto-regressive rollouts** of 2D velocity fields **(u, v)** around complex geometries (FPO / FlowBench).
You have two runnable apps:
- **`app_v1.py` (GT + metrics)**
- Uses `sample_cases/` containing the **full target sequence**.
- Produces:
- Ground truth vs prediction **GIFs** for u and v
- Relative L2 error vs time **plot** + **CSV**, where
\[
\mathrm{rel}\_L_2(t) = \frac{\lVert \hat{y}(t) - y(t) \rVert_2}{\lVert y(t) \rVert_2}.
\]
- **`app_v2.py` (prediction-only, arbitrary rollout length)**
- Uses `sample_cases/few_timesteps/` containing **only the first 16 GT frames** (enough to seed any checkpoint).
- Produces:
- Prediction **GIFs** for u and v for any number of rollout steps
- User chooses rollout length **N** (seeding uses only the first `s` frames).
## Sample format
Each sample uses two files:
- `sample_{id}_input.npy` → SDF geometry: **[1, H, W]**
- `sample_{id}_output.npy` → velocity sequence: **[T, 2, H, W]** (channels are u, v)
For **v2**, `T = 16` and files must be located in:
- `sample_cases/few_timesteps/`
## Checkpoints
Checkpoints are downloaded from the Hub at runtime:
- `checkpoints/time-dependent-deeponet_1in.ckpt`
- `checkpoints/time-dependent-deeponet_4in.ckpt`
- `checkpoints/time-dependent-deeponet_8in.ckpt`
- `checkpoints/time-dependent-deeponet_16in.ckpt`
Repo ID used by both apps:
```text
arabeh/DeepONet-FlowBench-FPO
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