| import os |
| from typing import Dict |
|
|
| import torch |
| import typeguard |
| from jaxtyping import jaxtyped |
|
|
| from pdeinvbench.utils.types import PDE |
|
|
| """ |
| Hardcoded parameter normalization stats for each dataset. |
| These are used to normalize the parameters before training. |
| """ |
| PARAM_NORMALIZATION_STATS = { |
| PDE.ReactionDiffusion2D: { |
| "k": (0.06391126306498819, 0.029533048151465856), |
| "Du": (0.3094992685910578, 0.13865605073673604), |
| "Dv": (0.259514500345804, 0.11541850276902947), |
| }, |
| PDE.NavierStokes2D: {"re": (1723.425, 1723.425)}, |
| PDE.TurbulentFlow2D: {"nu": (0.001372469573118451, 0.002146258280849241)}, |
| PDE.KortewegDeVries1D: {"delta": (2.899999997019768, 1.2246211546444339)}, |
| } |
|
|
|
|
| @jaxtyped(typechecker=typeguard.typechecked) |
| def unnormalize_params( |
| param_dict: Dict[str, torch.Tensor], pde: PDE |
| ) -> Dict[str, torch.Tensor]: |
| """ |
| Unnormalize the PDE parameters. |
| """ |
| for param in param_dict.keys(): |
| if "var" not in param: |
| mean, std = PARAM_NORMALIZATION_STATS[pde][param] |
| param_dict[param] = param_dict[param] * std + mean |
| return param_dict |
|
|
|
|
| @jaxtyped(typechecker=typeguard.typechecked) |
| def extract_params_from_path(path: str, pde: PDE) -> dict: |
| """ |
| Extracts the PDE parameters from the h5 path and returns as a dictionary. |
| """ |
| param_dict = {} |
| if pde == PDE.ReactionDiffusion2D: |
| name = os.path.basename(path) |
| elements = name.split("=")[1:] |
| Du = torch.Tensor([float(elements[0].split("_")[0])]) |
| Dv = torch.Tensor([float(elements[1].split("_")[0])]) |
| k = torch.Tensor( |
| [float(elements[2].split(".")[0] + "." + elements[2].split(".")[1])] |
| ) |
| param_dict = {"k": k, "Du": Du, "Dv": Dv} |
| elif pde == PDE.NavierStokes2D: |
| name = os.path.basename(path) |
| re_string = name.split(".")[0].strip() |
| re = torch.Tensor([float(re_string)]) |
| param_dict = {"re": re} |
| elif pde == PDE.TurbulentFlow2D: |
| name = os.path.basename(path) |
| viscosity_string = name.split("=")[1][:-3] |
| viscosity = float(viscosity_string) |
| param_dict = {"nu": torch.Tensor([viscosity])} |
| elif pde == PDE.KortewegDeVries1D: |
| name = os.path.basename(path) |
| delta = name.split("=")[-1].split("_")[0] |
| param_dict = {"delta": torch.Tensor([float(delta)])} |
| elif pde == PDE.DarcyFlow2D: |
| |
| name = os.path.basename(path) |
| index = name.split(".")[0].split("_")[-1] |
| index = int(index) |
| index = torch.Tensor([index]) |
| param_dict = {"index": index} |
| else: |
| raise ValueError(f"Unknown PDE type: {pde}. Cannot extract parameters.") |
|
|
| if len(param_dict) == 0: |
| raise ValueError( |
| f"No parameters found for PDE: {pde}. Cannot extract parameters." |
| ) |
| return param_dict |
|
|
