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40b99fb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 | from __future__ import annotations
from collections.abc import Mapping
from pathlib import Path
import numpy as np
import scipy.io
import torch
REQUIRED_FIELDS = {
"x_f1",
"y_f1",
"x_f2",
"y_f2",
"x_f3",
"y_f3",
"xi1",
"yi1",
"xi2",
"yi2",
"xb",
"yb",
"ub",
"u_exact",
"u_exact1",
"u_exact2",
"u_exact3",
}
def load_mat_data(path: Path) -> dict:
if not path.is_file():
raise FileNotFoundError(f"XPINN MATLAB data not found: {path}")
data = scipy.io.loadmat(path)
missing = REQUIRED_FIELDS.difference(data)
if missing:
raise ValueError(f"MATLAB data is missing fields: {sorted(missing)}")
return data
def column(data: Mapping, key: str) -> np.ndarray:
return np.asarray(data[key], dtype=np.float64).reshape(-1, 1)
def sample_indices(
generator: np.random.Generator, total_size: int, sample_size: int, name: str
) -> np.ndarray:
if sample_size <= 0:
raise ValueError(f"{name} sample size must be positive")
if sample_size > total_size:
raise ValueError(
f"{name} sample size {sample_size} exceeds available points {total_size}"
)
return generator.choice(total_size, sample_size, replace=False)
def tensor(
values: np.ndarray,
device: torch.device,
dtype: torch.dtype,
requires_grad: bool = False,
) -> torch.Tensor:
return torch.as_tensor(values, dtype=dtype, device=device).clone().requires_grad_(
requires_grad
)
def paired_sample(
data: Mapping,
x_key: str,
y_key: str,
sample_size: int,
generator: np.random.Generator,
device: torch.device,
dtype: torch.dtype,
name: str,
) -> tuple[torch.Tensor, torch.Tensor]:
x = column(data, x_key)
y = column(data, y_key)
if x.shape != y.shape:
raise ValueError(f"coordinate shape mismatch for {name}: {x.shape} and {y.shape}")
indices = sample_indices(generator, x.shape[0], sample_size, name)
return (
tensor(x[indices], device, dtype, requires_grad=True),
tensor(y[indices], device, dtype, requires_grad=True),
)
def build_training_batch(
data: Mapping,
sample_counts: Mapping[str, int],
seed: int,
device: torch.device,
dtype: torch.dtype,
) -> dict[str, torch.Tensor]:
generator = np.random.default_rng(seed)
x1, y1 = paired_sample(
data,
"x_f1",
"y_f1",
int(sample_counts["residual_1"]),
generator,
device,
dtype,
"residual_1",
)
x2, y2 = paired_sample(
data,
"x_f2",
"y_f2",
int(sample_counts["residual_2"]),
generator,
device,
dtype,
"residual_2",
)
x3, y3 = paired_sample(
data,
"x_f3",
"y_f3",
int(sample_counts["residual_3"]),
generator,
device,
dtype,
"residual_3",
)
xi1, yi1 = paired_sample(
data,
"xi1",
"yi1",
int(sample_counts["interface_1"]),
generator,
device,
dtype,
"interface_1",
)
xi2, yi2 = paired_sample(
data,
"xi2",
"yi2",
int(sample_counts["interface_2"]),
generator,
device,
dtype,
"interface_2",
)
boundary_x = column(data, "xb")
boundary_y = column(data, "yb")
boundary_values = column(data, "ub")
if boundary_x.shape != boundary_y.shape or boundary_x.shape != boundary_values.shape:
raise ValueError("boundary coordinate and value shapes do not match")
boundary_indices = sample_indices(
generator,
boundary_x.shape[0],
int(sample_counts["boundary"]),
"boundary",
)
return {
"xb": tensor(boundary_x[boundary_indices], device, dtype),
"yb": tensor(boundary_y[boundary_indices], device, dtype),
"ub": tensor(boundary_values[boundary_indices], device, dtype),
"x1": x1,
"y1": y1,
"x2": x2,
"y2": y2,
"x3": x3,
"y3": y3,
"xi1": xi1,
"yi1": yi1,
"xi2": xi2,
"yi2": yi2,
}
def build_evaluation_points(
data: Mapping, device: torch.device, dtype: torch.dtype
) -> dict[str, torch.Tensor]:
points = {}
for domain in (1, 2, 3):
x = column(data, f"x_f{domain}")
y = column(data, f"y_f{domain}")
if x.shape != y.shape:
raise ValueError(f"evaluation coordinate mismatch in domain {domain}")
points[f"xy{domain}"] = tensor(np.hstack((x, y)), device, dtype)
return points
def exact_subdomain_values(
data: Mapping, device: torch.device, dtype: torch.dtype
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
return tuple(
tensor(column(data, f"u_exact{domain}"), device, dtype)
for domain in (1, 2, 3)
)
def combined_coordinates(data: Mapping) -> tuple[np.ndarray, np.ndarray]:
x = np.concatenate([column(data, f"x_f{domain}").reshape(-1) for domain in (1, 2, 3)])
y = np.concatenate([column(data, f"y_f{domain}").reshape(-1) for domain in (1, 2, 3)])
return x, y
def combined_exact_solution(data: Mapping) -> np.ndarray:
exact = column(data, "u_exact").reshape(-1)
expected_size = sum(column(data, f"x_f{domain}").size for domain in (1, 2, 3))
if exact.size != expected_size:
raise ValueError(
f"combined exact solution has {exact.size} values, expected {expected_size}"
)
return exact
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