- class modulus.domain.validator.continuous.PointVTKValidator(vtk_obj: VTKBase, nodes: List[Node], input_vtk_map: Dict[str, List[str]], true_vtk_map: Dict[str, List[str]], invar: Dict[str, array] = {}, true_outvar: Dict[str, array] = {}, batch_size: int = 1024, plotter: Optional[ValidatorPlotter] = None, requires_grad: bool = False, log_iter: bool = False)[source]
Bases:
PointwiseValidatorPointwise validator using mesh points of VTK object
- Parameters
vtk_obj (VTKBase) – Modulus VTK object to use point locations from
nodes (List[Node]) – List of Modulus Nodes to unroll graph with.
input_vtk_map (Dict[str, List[str]]) – Dictionary mapping from Modulus input variables to VTK variable names {“modulus name”: [“vtk name”]}. Use colons to denote components of multi-dimensional VTK arrays (“name”:# )
true_vtk_map (Dict[str, List[str]]) – Dictionary mapping from Modulus target variables to VTK variable names {“modulus name”: [“vtk name”]}.
invar (Dict[str, np.array], optional) – Dictionary of additional numpy arrays as input, by default {}
true_outvar (Dict[str, np.array], optional) – Dictionary of additional numpy arrays used to validate against validation, by default {}
batch_size (int) – Batch size used when running validation.
plotter (ValidatorPlotter) – Modulus plotter for showing results in tensorboard.
requires_grad (bool, optional) – If automatic differentiation is needed for computing results., by default True
log_iter (bool, optional) – Save results to different file each call, by default False
- class modulus.domain.validator.continuous.PointwiseValidator(nodes: List[Node], invar: Dict[str, array], true_outvar: Dict[str, array], batch_size: int = 1024, plotter: Optional[ValidatorPlotter] = None, requires_grad: bool = False)[source]
Bases:
ValidatorPointwise Validator that allows walidating on pointwise data
- Parameters
nodes (List[Node]) – List of Modulus Nodes to unroll graph with.
invar (Dict[str, np.ndarray (N, 1)]) – Dictionary of numpy arrays as input.
true_outvar (Dict[str, np.ndarray (N, 1)]) – Dictionary of numpy arrays used to validate against validation.
batch_size (int, optional) – Batch size used when running validation, by default 1024
plotter (ValidatorPlotter) – Modulus plotter for showing results in tensorboard.
requires_grad (bool = False) – If automatic differentiation is needed for computing results.
- class modulus.domain.validator.discrete.DeepONet_Data_Validator(nodes: List[Node], invar_branch: Dict[str, array], invar_trunk: Dict[str, array], true_outvar: Dict[str, array], batch_size: int = 100, plotter: Optional[DeepONetValidatorPlotter] = None, requires_grad: bool = False)[source]
Bases: _DeepONet_Validator
DeepONet Validator
- class modulus.domain.validator.discrete.DeepONet_Physics_Validator(nodes: List[Node], invar_branch: Dict[str, array], invar_trunk: Dict[str, array], true_outvar: Dict[str, array], batch_size: int = 100, plotter: Optional[DeepONetValidatorPlotter] = None, requires_grad: bool = False, tile_trunk_input: bool = True)[source]
Bases: _DeepONet_Validator
DeepONet Validator
- class modulus.domain.validator.discrete.GridValidator(nodes: List[Node], dataset: Dataset, batch_size: int = 100, plotter: Optional[GridValidatorPlotter] = None, requires_grad: bool = False, num_workers: int = 0)[source]
Bases:
ValidatorData-driven grid field validator
- Parameters
nodes (List[Node]) – List of Modulus Nodes to unroll graph with.
dataset (Dataset) – dataset which contains invar and true outvar examples
batch_size (int, optional) – Batch size used when running validation, by default 100
plotter (GridValidatorPlotter) – Modulus plotter for showing results in tensorboard.
requires_grad (bool = False) – If automatic differentiation is needed for computing results.
num_workers (int, optional) – Number of dataloader workers, by default 0
- class modulus.domain.validator.validator.Validator[source]
Bases: object
Validator base class