Datasets:

n3il666
/

NeFTY

---
license: mit
pretty_name: NeFTY Synthetic Thermography Benchmark
tags:
  - physics
  - inverse-problems
  - thermography
  - neural-fields
  - heat-conduction
  - non-destructive-evaluation
size_categories:
  - 1K<n<10K
---
# NeFTY Synthetic Thermography Benchmark

Data and pretrained baseline for **Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation** (NeurIPS 2026).

[Project page](https://cab-lab-princeton.github.io/nefty/) | [Paper (arXiv 2603.11045)](https://arxiv.org/abs/2603.11045) | [Code](https://github.com/CAB-Lab-Princeton/NeFTY-code)

The benchmark poses label-free 3D inverse heat conduction: given the transient top-surface temperature of a slab after a pulsed heat input, recover the full 3D thermal diffusivity field, including subsurface defects that are never observed directly. Every sample pairs the observed surface thermogram with its exact 3D ground-truth diffusivity, so volumetric reconstructions can be scored voxel by voxel.

## Files

| File | Content | Size (bytes) | SHA-256 |
|---|---|---|---|
| `synthetic_1000/dataset.pt` | 1,000-sample benchmark (homogeneous and layered, with defects) | 3,833,858,333 | `f3c7c73d1b8e61f0dd0bf380cd7b6ecedbf631499ba1ed5de461ecdc20e37f85` |
| `synthetic_1000_nodefect/dataset.pt` | 1,000 defect-free samples (training set of the Sound-Only U-Net baseline) | 3,833,858,397 | `fe504f8374da03f0f48750b6a8bacb720e8abdebef7b77efda62a4de19a3d887` |
| `checkpoints/unet_sound_only.tar` | pretrained Sound-Only 3D U-Net | 84,089,427 | `11d53863253d1fa75955cdd759b4bee62b8162fd4b1314cfc73513e8833aecff` |

## Benchmark generation

Ground truth is simulated with PhiFlow (Holl and Thuerey, ICML 2024), a finite-volume engine independent of the implicit-Euler solver used by NeFTY, so reconstructions are not evaluated against data produced by their own discretization. PhiFlow integrates the heat equation explicitly with adaptive sub-stepping.

- **Domain and grid.** Unitless slab [0, 10]<sup>2</sup> × [0, 1] on a 64 × 64 × 16 grid.
- **Boundary conditions.** Periodic on the lateral faces and adiabatic on the top and bottom faces.
- **Initial condition.** A Gaussian post-flash temperature profile localized near the front face, identical for all samples.
- **Time axis.** 100 recorded frames at Δt = 0.05. The stored surface sequences also include the initial frame, so each holds 101 frames.
- **Materials.** Bulk diffusivity α<sub>base</sub> ~ U(0.1, 0.2) and defect diffusivity α<sub>defect</sub> ~ U(0.005, 0.015).
- **Defects.** One to four ellipsoidal, cylindrical, or box-shaped subsurface defects per sample.
- **Bulk configurations.** Homogeneous (constant α<sub>base</sub>) or layered (three or four strata along z, each with its own bulk diffusivity).

### Index layout

`synthetic_1000/dataset.pt`:

| Configuration | Indices | Samples |
|---|---|---|
| Homogeneous, 1 defect | 0-199 | 200 |
| Homogeneous, 2 defects | 200-399 | 200 |
| Homogeneous, 3 defects | 400-600 | 201 |
| Homogeneous, 4 defects | 601-799 | 199 |
| 3 layers, 1 / 2 / 3 / 4 defects | 800-825 / 826-850 / 851-875 / 876-900 | 101 |
| 4 layers, 1 / 2 / 3 / 4 defects | 901-924 / 925-950 / 951-975 / 976-999 | 99 |

The generator assigns categories from the fractional index i / 1000 in floating point, so some bucket edges fall one sample away from round numbers. Indices 0-799 form the homogeneous split (800 samples) and 800-999 the layered split (200 samples). `synthetic_1000_nodefect/dataset.pt` uses the same bulk layout without defects: 0-799 homogeneous, 800-900 three layers, 901-999 four layers.

## Data format

Each `dataset.pt` is a Python dictionary saved with `torch.save` (N = 1000):

| Key | Shape | dtype | Content |
|---|---|---|---|
| `k_gt` | (N, 64, 64, 16) | float32 | ground-truth diffusivity on the (x, y, z) cell-center grid |
| `t0_gt` | (N, 64, 64, 16) | float32 | initial temperature (Gaussian post-flash profile) |
| `surface_gt` | (N, 101, 64, 64) | float32 | observed top-surface temperature, frames 0 to 100 (equal to `surface_gt_phiflow`) |
| `surface_gt_phiflow` | (N, 101, 64, 64) | float32 | PhiFlow ground-truth surface temperature |
| `surface_gt_ours` | (N, 101, 64, 64) | float32 | NeFTY solver's forward prediction at `k_gt` (reference only) |
| `resolution` | dict | | `{'x': 64, 'y': 64, 'z': 16}` |

## Usage

```python
import torch
from huggingface_hub import hf_hub_download
path = hf_hub_download(repo_id="n3il666/NeFTY", filename="synthetic_1000/dataset.pt", repo_type="dataset")
data = torch.load(path)
alpha = data["k_gt"][0]          # (64, 64, 16) ground-truth diffusivity of sample 0
surface = data["surface_gt"][0]  # (101, 64, 64) observed surface temperature of sample 0
```

The [code repository](https://github.com/CAB-Lab-Princeton/NeFTY-code) downloads every file to the expected location with `python scripts/download_data.py`, reconstructs individual specimens or the full benchmark with NeFTY and the baselines, and regenerates both datasets with `python -m dataset.synthetic` and `python -m dataset.generate_defect_free` (seed 42).

## Sound-Only U-Net checkpoint

`checkpoints/unet_sound_only.tar` holds a 3D U-Net (four-level encoder-decoder with channel sizes 32, 64, 128, 256) that maps the surface temperature sequence to a 3D diffusivity field, with a sigmoid output scaled to [0.003, 0.25]. It is trained with MSE supervision only on the defect-free set, so it serves as a data-driven, zero-shot reference that probes generalization to unseen defects. The file is a dictionary with `model_state_dict`, `optimizer_state_dict`, `scheduler_state_dict`, `global_step`, and `args`. Evaluate it on the benchmark with `evaluate_unet.py` or `evaluate_unet_batch.py` from the code repository.

## Citation

```bibtex
@inproceedings{zhong2026nefty,
  title     = {Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation},
  author    = {Zhong, Tao and Hu, Yixun and Zheng, Dongzhe and Sood, Aditya and Allen-Blanchette, Christine},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year      = {2026}
}
```

## License

Released under the MIT License.

## Contact

Tao Zhong (tzhong@princeton.edu), Princeton University.

	---
	license: mit
	pretty_name: NeFTY Synthetic Thermography Benchmark
	tags:
	- physics
	- inverse-problems
	- thermography
	- neural-fields
	- heat-conduction
	- non-destructive-evaluation
	size_categories:
	- 1K<n<10K
	---

	# NeFTY Synthetic Thermography Benchmark

	Data and pretrained baseline for Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation (NeurIPS 2026).

	[Project page](https://cab-lab-princeton.github.io/nefty/) \| [Paper (arXiv 2603.11045)](https://arxiv.org/abs/2603.11045) \| [Code](https://github.com/CAB-Lab-Princeton/NeFTY-code)

	The benchmark poses label-free 3D inverse heat conduction: given the transient top-surface temperature of a slab after a pulsed heat input, recover the full 3D thermal diffusivity field, including subsurface defects that are never observed directly. Every sample pairs the observed surface thermogram with its exact 3D ground-truth diffusivity, so volumetric reconstructions can be scored voxel by voxel.

	## Files

	\| File \| Content \| Size (bytes) \| SHA-256 \|
	\|---\|---\|---\|---\|
	\| `synthetic_1000/dataset.pt` \| 1,000-sample benchmark (homogeneous and layered, with defects) \| 3,833,858,333 \| `f3c7c73d1b8e61f0dd0bf380cd7b6ecedbf631499ba1ed5de461ecdc20e37f85` \|
	\| `synthetic_1000_nodefect/dataset.pt` \| 1,000 defect-free samples (training set of the Sound-Only U-Net baseline) \| 3,833,858,397 \| `fe504f8374da03f0f48750b6a8bacb720e8abdebef7b77efda62a4de19a3d887` \|
	\| `checkpoints/unet_sound_only.tar` \| pretrained Sound-Only 3D U-Net \| 84,089,427 \| `11d53863253d1fa75955cdd759b4bee62b8162fd4b1314cfc73513e8833aecff` \|

	## Benchmark generation

	Ground truth is simulated with PhiFlow (Holl and Thuerey, ICML 2024), a finite-volume engine independent of the implicit-Euler solver used by NeFTY, so reconstructions are not evaluated against data produced by their own discretization. PhiFlow integrates the heat equation explicitly with adaptive sub-stepping.

	- Domain and grid. Unitless slab [0, 10]<sup>2</sup> × [0, 1] on a 64 × 64 × 16 grid.
	- Boundary conditions. Periodic on the lateral faces and adiabatic on the top and bottom faces.
	- Initial condition. A Gaussian post-flash temperature profile localized near the front face, identical for all samples.
	- Time axis. 100 recorded frames at Δt = 0.05. The stored surface sequences also include the initial frame, so each holds 101 frames.
	- Materials. Bulk diffusivity α<sub>base</sub> ~ U(0.1, 0.2) and defect diffusivity α<sub>defect</sub> ~ U(0.005, 0.015).
	- Defects. One to four ellipsoidal, cylindrical, or box-shaped subsurface defects per sample.
	- Bulk configurations. Homogeneous (constant α<sub>base</sub>) or layered (three or four strata along z, each with its own bulk diffusivity).

	### Index layout

	`synthetic_1000/dataset.pt`:

	\| Configuration \| Indices \| Samples \|
	\|---\|---\|---\|
	\| Homogeneous, 1 defect \| 0-199 \| 200 \|
	\| Homogeneous, 2 defects \| 200-399 \| 200 \|
	\| Homogeneous, 3 defects \| 400-600 \| 201 \|
	\| Homogeneous, 4 defects \| 601-799 \| 199 \|
	\| 3 layers, 1 / 2 / 3 / 4 defects \| 800-825 / 826-850 / 851-875 / 876-900 \| 101 \|
	\| 4 layers, 1 / 2 / 3 / 4 defects \| 901-924 / 925-950 / 951-975 / 976-999 \| 99 \|

	The generator assigns categories from the fractional index i / 1000 in floating point, so some bucket edges fall one sample away from round numbers. Indices 0-799 form the homogeneous split (800 samples) and 800-999 the layered split (200 samples). `synthetic_1000_nodefect/dataset.pt` uses the same bulk layout without defects: 0-799 homogeneous, 800-900 three layers, 901-999 four layers.

	## Data format

	Each `dataset.pt` is a Python dictionary saved with `torch.save` (N = 1000):

	\| Key \| Shape \| dtype \| Content \|
	\|---\|---\|---\|---\|
	\| `k_gt` \| (N, 64, 64, 16) \| float32 \| ground-truth diffusivity on the (x, y, z) cell-center grid \|
	\| `t0_gt` \| (N, 64, 64, 16) \| float32 \| initial temperature (Gaussian post-flash profile) \|
	\| `surface_gt` \| (N, 101, 64, 64) \| float32 \| observed top-surface temperature, frames 0 to 100 (equal to `surface_gt_phiflow`) \|
	\| `surface_gt_phiflow` \| (N, 101, 64, 64) \| float32 \| PhiFlow ground-truth surface temperature \|
	\| `surface_gt_ours` \| (N, 101, 64, 64) \| float32 \| NeFTY solver's forward prediction at `k_gt` (reference only) \|
	\| `resolution` \| dict \| \| `{'x': 64, 'y': 64, 'z': 16}` \|

	## Usage

	```python
	import torch
	from huggingface_hub import hf_hub_download

	path = hf_hub_download(repo_id="n3il666/NeFTY", filename="synthetic_1000/dataset.pt", repo_type="dataset")
	data = torch.load(path)

	alpha = data["k_gt"][0] # (64, 64, 16) ground-truth diffusivity of sample 0
	surface = data["surface_gt"][0] # (101, 64, 64) observed surface temperature of sample 0
	```

	The [code repository](https://github.com/CAB-Lab-Princeton/NeFTY-code) downloads every file to the expected location with `python scripts/download_data.py`, reconstructs individual specimens or the full benchmark with NeFTY and the baselines, and regenerates both datasets with `python -m dataset.synthetic` and `python -m dataset.generate_defect_free` (seed 42).

	## Sound-Only U-Net checkpoint

	`checkpoints/unet_sound_only.tar` holds a 3D U-Net (four-level encoder-decoder with channel sizes 32, 64, 128, 256) that maps the surface temperature sequence to a 3D diffusivity field, with a sigmoid output scaled to [0.003, 0.25]. It is trained with MSE supervision only on the defect-free set, so it serves as a data-driven, zero-shot reference that probes generalization to unseen defects. The file is a dictionary with `model_state_dict`, `optimizer_state_dict`, `scheduler_state_dict`, `global_step`, and `args`. Evaluate it on the benchmark with `evaluate_unet.py` or `evaluate_unet_batch.py` from the code repository.

	## Citation

	```bibtex
	@inproceedings{zhong2026nefty,
	title = {Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation},
	author = {Zhong, Tao and Hu, Yixun and Zheng, Dongzhe and Sood, Aditya and Allen-Blanchette, Christine},
	booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
	year = {2026}
	}
	```

	## License

	Released under the MIT License.

	## Contact

	Tao Zhong (tzhong@princeton.edu), Princeton University.