noether.data.datasets.cfd.simshift_heatsink.dataset

Attributes

logger

Classes

SimshiftHeatsinkDataset

Dataset for the SIMSHIFT Heatsink CFD benchmark.

Module Contents

noether.data.datasets.cfd.simshift_heatsink.dataset.logger
class noether.data.datasets.cfd.simshift_heatsink.dataset.SimshiftHeatsinkDataset(dataset_config)

Bases: noether.data.Dataset

Dataset for the SIMSHIFT Heatsink CFD benchmark.

The SIMSHIFT Heatsink dataset contains conjugate heat transfer simulations of heatsink geometries with varying fin configurations. Data is stored in HDF5 format with mesh coordinates and element-level physical fields (velocity, temperature, pressure).

The dataset supports source/target domain splits at different difficulty levels for unsupervised domain adaptation experiments.

When root is not provided the dataset is downloaded from HuggingFace Hub and read directly from the zip archive (no extraction needed).

Reference: https://arxiv.org/abs/2506.12007

Parameters:

dataset_config (noether.data.datasets.cfd.simshift_heatsink.config.SimshiftHeatsinkConfig) – Configuration for the dataset. See DatasetBaseConfig for available options including dataset normalizers.

STATS_FILE: str = ''
difficulty
domain
split
pre_getitem(idx)

Load all fields for sample idx from its HDF5 file.

The returned dict is forwarded as kwargs to every getitem_* method.

Parameters:

idx (int)

Return type:

dict[str, torch.Tensor]

getitem_volume_position(idx, *, position, **_)

Element centre coordinates of the volume mesh (num_elements, 3).

Parameters:
Return type:

torch.Tensor

getitem_volume_velocity(idx, *, velocity, **_)

Velocity field at element centres (num_elements, 3).

Parameters:
Return type:

torch.Tensor

getitem_volume_temperature(idx, *, temperature, **_)

Temperature field at element centres (num_elements, 1).

Parameters:
Return type:

torch.Tensor

getitem_volume_pressure(idx, *, pressure, **_)

Pressure (p_rgh) field at element centres (num_elements, 1).

Parameters:
Return type:

torch.Tensor

getitem_simulation_parameters(idx)

Geometry design parameters conditioning vector (num_params,).

Parameters:

idx (int)

Return type:

torch.Tensor

sample_info(idx)

Get information about a sample such as its path, sample ID, etc.

Parameters:

idx (int)

Return type:

dict[str, str | int | None]