|
|
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 |
|
|
|
