[1] Dittmer, Sören, Erzmann, David, Harms, Henrik, Falck, Rielson, & Gosch, Marco. (2023). SELTO Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7781392
class SELTODataset[source]
SELTODataset(root:str,name:str,train:bool=True,size:int=-1,download:bool=True,verbose:bool=True,dtype:dtype=torch.float32,pde_solver:dl4to.pde.PDESolver=None,solve_pde_for_trivial_solution:bool=False,solve_pde_for_gt_solution:bool=False) ::CSVDataset
A class for downloading, generating and importing the SELTO datasets [1].
| Type | Default | Details | |
|---|---|---|---|
root |
str |
The root directory in which the datasets should be downloaded, generated and accessed. | |
name |
str |
The name of the dataset that should be downloaded. | |
train |
bool |
True |
Whether the training or validation dataset should be generated. |
size |
int |
-1 |
The size of the dataset. If size=-1, then the whole dataset is imported. Useful if only subsets of the original dataset are needed. |
download |
bool |
True |
Whether the dataset should be downloaded, if needed. |
verbose |
bool |
True |
Whether to give the user feedback on the progress. |
dtype |
dtype |
torch.float32 |
The datatype into which the values from the csv files are converted. |
pde_solver |
dl4to.pde.PDESolver |
None |
The PDE solver that is used to solve the PDE for linear elasticity. Only has an effect if either solve_pde_for_trivial_solution=True or solve_pde_for_gt_solution=True. |
solve_pde_for_trivial_solution |
bool |
False |
Whether to solve the PDE for each trivial solution and save the displacements in the solution object. These can later be accessed via problem.trivial_solution.u. This is useful if PDE preprocessing is used. Requires a PDE solver. |
solve_pde_for_gt_solution |
bool |
False |
Whether to solve the PDE for each ground truth and save the displacements in the solution object. These can later be accessed via gt_solution.u. Requires a PDE solver. |
[1] Dittmer, Sören, Erzmann, David, Harms, Henrik, Falck, Rielson, & Gosch, Marco. (2023). SELTO Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7781392