noether.modeling.models.aerodynamics

Classes

AeroTransformerConfig

Transformer config extended with aerodynamic data specifications.

AeroTransolverConfig

Transolver config extended with aerodynamic data specifications.

AeroTransformer

Aerodynamic Transformer wrapper.

AeroTransolver

Aerodynamic Transolver wrapper.

AeroUPT

Aerodynamic UPT wrapper.

AeroABUPT

Aerodynamic Anchored-Branched UPT wrapper.

Module Contents

class noether.modeling.models.aerodynamics.AeroTransformerConfig(/, **data)

Bases: noether.core.schemas.models.TransformerConfig

Transformer config extended with aerodynamic data specifications.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

data_specs: noether.core.schemas.dataset.AeroDataSpecs
class noether.modeling.models.aerodynamics.AeroTransolverConfig(/, **data)

Bases: noether.core.schemas.models.TransolverConfig

Transolver config extended with aerodynamic data specifications.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

data_specs: noether.core.schemas.dataset.AeroDataSpecs
class noether.modeling.models.aerodynamics.AeroTransformer(model_config, **kwargs)

Bases: noether.core.models.Model

Aerodynamic Transformer wrapper.

End-to-end forward for aero CFD: positional encoding, optional RoPE, optional physics features, surface/volume bias, Transformer backbone, output projection, and output gathering.

Base class for single models, i.e. one model with one optimizer as opposed to CompositeModel.

Parameters:

  • model_config (AeroTransformerConfig) – Model configuration. See ModelBaseConfig for available options.

  • update_counter – The UpdateCounter provided to the optimizer.

  • is_frozen – If true, will set requires_grad of all parameters to false. Will also put the model into eval mode (e.g., to put Dropout or BatchNorm into eval mode).

  • path_provider – PathProvider used by the initializer to store or retrieve checkpoints.

  • data_container – DataContainer which includes the data and dataloader. This is currently unused but helpful for quick prototyping only, evaluating forward in debug mode, etc.

data_specs
use_rope
pos_embed
surface_bias
volume_bias
use_physics_features
backbone
norm
out
forward(surface_position, volume_position, surface_features=None, volume_features=None)
Parameters:
Return type:

dict[str, torch.Tensor]

class noether.modeling.models.aerodynamics.AeroTransolver(model_config, **kwargs)

Bases: noether.core.models.Model

Aerodynamic Transolver wrapper.

Like AeroTransformer but adds the Transolver-specific learnable placeholder parameter.

Base class for single models, i.e. one model with one optimizer as opposed to CompositeModel.

Parameters:

  • model_config (AeroTransolverConfig) – Model configuration. See ModelBaseConfig for available options.

  • update_counter – The UpdateCounter provided to the optimizer.

  • is_frozen – If true, will set requires_grad of all parameters to false. Will also put the model into eval mode (e.g., to put Dropout or BatchNorm into eval mode).

  • path_provider – PathProvider used by the initializer to store or retrieve checkpoints.

  • data_container – DataContainer which includes the data and dataloader. This is currently unused but helpful for quick prototyping only, evaluating forward in debug mode, etc.

data_specs
pos_embed
surface_bias
volume_bias
use_physics_features
placeholder
backbone
norm
out
forward(surface_position, volume_position, surface_features=None, volume_features=None)
Parameters:
Return type:

dict[str, torch.Tensor]

class noether.modeling.models.aerodynamics.AeroUPT(model_config, **kwargs)

Bases: noether.core.models.Model

Aerodynamic UPT wrapper.

Combines separate surface/volume query positions into the single query_position that the core UPT expects, and splits outputs using AeroDataSpecs.

Base class for single models, i.e. one model with one optimizer as opposed to CompositeModel.

Parameters:

  • model_config (noether.core.schemas.models.UPTConfig) – Model configuration. See ModelBaseConfig for available options.

  • update_counter – The UpdateCounter provided to the optimizer.

  • is_frozen – If true, will set requires_grad of all parameters to false. Will also put the model into eval mode (e.g., to put Dropout or BatchNorm into eval mode).

  • path_provider – PathProvider used by the initializer to store or retrieve checkpoints.

  • data_container – DataContainer which includes the data and dataloader. This is currently unused but helpful for quick prototyping only, evaluating forward in debug mode, etc.

backbone
data_specs
forward(surface_position_batch_idx, surface_position_supernode_idx, surface_position, surface_query_position, volume_query_position)
Parameters:
Return type:

dict[str, torch.Tensor]

class noether.modeling.models.aerodynamics.AeroABUPT(model_config, **kwargs)

Bases: noether.core.models.Model

Aerodynamic Anchored-Branched UPT wrapper.

Bridges the factory’s (config, **kwargs) instantiation pattern to the core model.

Base class for single models, i.e. one model with one optimizer as opposed to CompositeModel.

Parameters:

  • model_config (noether.core.schemas.models.AnchorBranchedUPTConfig) – Model configuration. See ModelBaseConfig for available options.

  • update_counter – The UpdateCounter provided to the optimizer.

  • is_frozen – If true, will set requires_grad of all parameters to false. Will also put the model into eval mode (e.g., to put Dropout or BatchNorm into eval mode).

  • path_provider – PathProvider used by the initializer to store or retrieve checkpoints.

  • data_container – DataContainer which includes the data and dataloader. This is currently unused but helpful for quick prototyping only, evaluating forward in debug mode, etc.

backbone
forward(**kwargs)
Return type:

dict[str, torch.Tensor]