diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 785077a..99b9764 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -35,6 +35,11 @@ jobs:
       - name: Upgrade pip
         run: python -m pip install --upgrade pip
 
+              - name: Install PyTorch
+        run: >
+          python -m pip install torch
+          --index-url https://download.pytorch.org/whl/cpu
+          
       - name: Install CalculiX
         run: |
           sudo apt-get update
diff --git a/.gitignore b/.gitignore
index 97d2161..796d288 100644
--- a/.gitignore
+++ b/.gitignore
@@ -225,6 +225,9 @@ __pycache__/
 # Virtual environment
 .venv/
 
+# ML model checkpoints
+models/checkpoints/
+
 # Testing
 .pytest_cache/
 .coverage
diff --git a/README.md b/README.md
index b6199a1..3cfbad5 100644
--- a/README.md
+++ b/README.md
@@ -136,4 +136,31 @@ The initial implementation does not yet include:
 - notch effects
 - multiaxial fatigue
 - low-cycle fatigue
-- crack-growth modelling
\ No newline at end of file
+- crack-growth modelling
+
+## PyTorch Structural Surrogate
+
+BodySimPy includes a feed-forward neural-network surrogate trained on a six-dimensional CalculiX design dataset.
+
+### Inputs
+
+- wall thickness
+- section height
+- section width
+- Young's modulus
+- material density
+- applied tip force
+
+### Predicted responses
+
+- maximum axial stress
+- tip displacement
+- mode-1 natural frequency
+
+The training dataset is generated using Latin Hypercube sampling across a bounded engineering design space and evaluated using automated static and modal CalculiX analyses.
+
+The dataset is separated into training, validation and held-out test subsets. Input and target normalization statistics are fitted exclusively on the training subset.
+
+Training includes validation monitoring, early stopping and best-model checkpointing.
+
+The surrogate is intended for interpolation within the sampled design space and does not replace finite-element validation outside that domain.
\ No newline at end of file
diff --git a/data/surrogate/fea_surrogate_dataset.csv b/data/surrogate/fea_surrogate_dataset.csv
new file mode 100644
index 0000000..3a4ee45
--- /dev/null
+++ b/data/surrogate/fea_surrogate_dataset.csv
@@ -0,0 +1,501 @@
+sample,thickness_m,height_m,width_m,youngs_modulus_pa,density_kg_m3,tip_force_n,max_stress_pa,tip_deflection_m,mode_1_frequency_hz
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diff --git a/docs/figures/fea_vs_predicted_deflection.png b/docs/figures/fea_vs_predicted_deflection.png
new file mode 100644
index 0000000..3cd7964
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diff --git a/docs/figures/fea_vs_predicted_frequency.png b/docs/figures/fea_vs_predicted_frequency.png
new file mode 100644
index 0000000..c3a2cc5
Binary files /dev/null and b/docs/figures/fea_vs_predicted_frequency.png differ
diff --git a/docs/figures/fea_vs_predicted_stress.png b/docs/figures/fea_vs_predicted_stress.png
new file mode 100644
index 0000000..8044472
Binary files /dev/null and b/docs/figures/fea_vs_predicted_stress.png differ
diff --git a/docs/figures/ml_training_validation_loss.png b/docs/figures/ml_training_validation_loss.png
new file mode 100644
index 0000000..b0a6258
Binary files /dev/null and b/docs/figures/ml_training_validation_loss.png differ
diff --git a/docs/figures/prediction_error_distribution.png b/docs/figures/prediction_error_distribution.png
new file mode 100644
index 0000000..4575dce
Binary files /dev/null and b/docs/figures/prediction_error_distribution.png differ
diff --git a/docs/validation/ml_test_metrics.csv b/docs/validation/ml_test_metrics.csv
new file mode 100644
index 0000000..14a6e98
--- /dev/null
+++ b/docs/validation/ml_test_metrics.csv
@@ -0,0 +1,4 @@
+response,mae,rmse,unit,mape_percent
+max_stress,1.9490350221438695,2.932099727218547,MPa,1.0589989299689422
+tip_deflection,0.18823362033881488,0.23935947693489001,mm,1.2683177190784598
+mode_1_frequency,0.18998264970865583,0.23496431869832404,Hz,0.39162889707918835
diff --git a/docs/validation/ml_test_predictions.csv b/docs/validation/ml_test_predictions.csv
new file mode 100644
index 0000000..bd56c36
--- /dev/null
+++ b/docs/validation/ml_test_predictions.csv
@@ -0,0 +1,76 @@
+dataset_index,fea_stress_mpa,predicted_stress_mpa,stress_error_percent,fea_deflection_mm,predicted_deflection_mm,deflection_error_percent,fea_mode_1_hz,predicted_mode_1_hz,frequency_error_percent
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diff --git a/pyproject.toml b/pyproject.toml
index e514ca7..fed3283 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,6 +31,12 @@ dev = [
     "ruff>=0.5",
     "mypy>=1.10",
     "types-PyYAML",
+    "scipy-stubs",
+    "pandas-stubs",
+]
+
+ml = [
+    "torch>=2.7",
 ]
 
 [project.scripts]
diff --git a/scripts/evaluate_surrogate.py b/scripts/evaluate_surrogate.py
new file mode 100644
index 0000000..ca0a8f8
--- /dev/null
+++ b/scripts/evaluate_surrogate.py
@@ -0,0 +1,474 @@
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from numpy.typing import NDArray
+
+from bodysimpy.ml.dataset import (
+    load_surrogate_arrays,
+)
+from bodysimpy.ml.evaluation import (
+    TargetMetrics,
+    calculate_metrics,
+    calculate_percentage_errors,
+    predict,
+)
+from bodysimpy.ml.training import (
+    TrainingResult,
+    train_surrogate,
+)
+
+DATASET_PATH = Path("data/surrogate/fea_surrogate_dataset.csv")
+
+FIGURE_DIRECTORY = Path("docs/figures")
+
+VALIDATION_DIRECTORY = Path("docs/validation")
+
+
+def save_loss_plot(
+    result: TrainingResult,
+) -> None:
+    """Plot training and validation loss versus epoch."""
+
+    epochs = np.arange(
+        1,
+        len(result.train_losses) + 1,
+    )
+
+    plt.figure(figsize=(8, 5))
+
+    plt.plot(
+        epochs,
+        result.train_losses,
+        label="Training loss",
+    )
+
+    plt.plot(
+        epochs,
+        result.validation_losses,
+        label="Validation loss",
+    )
+
+    plt.axvline(
+        result.best_epoch,
+        linestyle="--",
+        label=f"Best epoch: {result.best_epoch}",
+    )
+
+    plt.xlabel("Epoch")
+    plt.ylabel("Normalized MSE")
+    plt.title("BodySimPy Surrogate Training History")
+
+    plt.legend()
+    plt.grid(True)
+    plt.tight_layout()
+
+    plt.savefig(
+        FIGURE_DIRECTORY / "ml_training_validation_loss.png",
+        dpi=200,
+    )
+
+    plt.close()
+
+
+def save_prediction_scatter(
+    reference: NDArray[np.float64],
+    prediction: NDArray[np.float64],
+    *,
+    x_label: str,
+    y_label: str,
+    title: str,
+    output_path: Path,
+) -> None:
+    """Create an FEA-versus-surrogate prediction plot."""
+
+    lower_bound = float(
+        min(
+            np.min(reference),
+            np.min(prediction),
+        )
+    )
+
+    upper_bound = float(
+        max(
+            np.max(reference),
+            np.max(prediction),
+        )
+    )
+
+    plt.figure(figsize=(6, 6))
+
+    plt.scatter(
+        reference,
+        prediction,
+        alpha=0.75,
+    )
+
+    plt.plot(
+        [
+            lower_bound,
+            upper_bound,
+        ],
+        [
+            lower_bound,
+            upper_bound,
+        ],
+        linestyle="--",
+        label="Ideal prediction",
+    )
+
+    plt.xlabel(x_label)
+    plt.ylabel(y_label)
+    plt.title(title)
+
+    plt.legend()
+    plt.grid(True)
+    plt.tight_layout()
+
+    plt.savefig(
+        output_path,
+        dpi=200,
+    )
+
+    plt.close()
+
+
+def save_error_distribution(
+    stress_errors_percent: NDArray[np.float64],
+    deflection_errors_percent: NDArray[np.float64],
+    frequency_errors_percent: NDArray[np.float64],
+) -> None:
+    """Plot percentage-error distributions for all predicted responses."""
+
+    plt.figure(figsize=(9, 5))
+
+    plt.hist(
+        stress_errors_percent,
+        bins=20,
+        alpha=0.5,
+        label="Stress",
+    )
+
+    plt.hist(
+        deflection_errors_percent,
+        bins=20,
+        alpha=0.5,
+        label="Tip deflection",
+    )
+
+    plt.hist(
+        frequency_errors_percent,
+        bins=20,
+        alpha=0.5,
+        label="Mode-1 frequency",
+    )
+
+    plt.xlabel("Absolute percentage error [%]")
+    plt.ylabel("Test samples")
+
+    plt.title("BodySimPy Surrogate Prediction Error Distribution")
+
+    plt.legend()
+    plt.grid(True)
+    plt.tight_layout()
+
+    plt.savefig(
+        FIGURE_DIRECTORY / "prediction_error_distribution.png",
+        dpi=200,
+    )
+
+    plt.close()
+
+
+def save_metrics_csv(
+    *,
+    stress_metrics: TargetMetrics,
+    deflection_metrics: TargetMetrics,
+    frequency_metrics: TargetMetrics,
+) -> None:
+    """Store held-out test metrics in engineering units."""
+
+    dataframe = pd.DataFrame(
+        [
+            {
+                "response": "max_stress",
+                "mae": stress_metrics.mae / 1e6,
+                "rmse": stress_metrics.rmse / 1e6,
+                "unit": "MPa",
+                "mape_percent": (stress_metrics.mean_absolute_percentage_error),
+            },
+            {
+                "response": "tip_deflection",
+                "mae": deflection_metrics.mae * 1000.0,
+                "rmse": deflection_metrics.rmse * 1000.0,
+                "unit": "mm",
+                "mape_percent": (deflection_metrics.mean_absolute_percentage_error),
+            },
+            {
+                "response": "mode_1_frequency",
+                "mae": frequency_metrics.mae,
+                "rmse": frequency_metrics.rmse,
+                "unit": "Hz",
+                "mape_percent": (frequency_metrics.mean_absolute_percentage_error),
+            },
+        ]
+    )
+
+    dataframe.to_csv(
+        VALIDATION_DIRECTORY / "ml_test_metrics.csv",
+        index=False,
+    )
+
+
+def save_test_predictions(
+    *,
+    reference: NDArray[np.float64],
+    prediction: NDArray[np.float64],
+    test_indices: NDArray[np.int64],
+) -> None:
+    """Store every held-out FEA and PyTorch prediction."""
+
+    stress_error = calculate_percentage_errors(
+        reference[:, 0],
+        prediction[:, 0],
+    )
+
+    deflection_error = calculate_percentage_errors(
+        reference[:, 1],
+        prediction[:, 1],
+    )
+
+    frequency_error = calculate_percentage_errors(
+        reference[:, 2],
+        prediction[:, 2],
+    )
+
+    dataframe = pd.DataFrame(
+        {
+            "dataset_index": test_indices,
+            "fea_stress_mpa": (reference[:, 0] / 1e6),
+            "predicted_stress_mpa": (prediction[:, 0] / 1e6),
+            "stress_error_percent": (stress_error),
+            "fea_deflection_mm": (reference[:, 1] * 1000.0),
+            "predicted_deflection_mm": (prediction[:, 1] * 1000.0),
+            "deflection_error_percent": (deflection_error),
+            "fea_mode_1_hz": (reference[:, 2]),
+            "predicted_mode_1_hz": (prediction[:, 2]),
+            "frequency_error_percent": (frequency_error),
+        }
+    )
+
+    dataframe.to_csv(
+        VALIDATION_DIRECTORY / "ml_test_predictions.csv",
+        index=False,
+    )
+
+
+def print_metrics(
+    *,
+    stress_metrics: TargetMetrics,
+    deflection_metrics: TargetMetrics,
+    frequency_metrics: TargetMetrics,
+) -> None:
+    """Print held-out test-set metrics."""
+
+    print()
+    print("BodySimPy PyTorch Surrogate Evaluation")
+    print("=" * 78)
+
+    print()
+    print(f"{'Response':<20}{'MAE':>16}{'RMSE':>16}{'MAPE':>16}")
+
+    print("-" * 78)
+
+    print(
+        f"{'Stress':<20}"
+        f"{stress_metrics.mae / 1e6:>12.4f} MPa"
+        f"{stress_metrics.rmse / 1e6:>12.4f} MPa"
+        f"{stress_metrics.mean_absolute_percentage_error:>14.3f} %"
+    )
+
+    print(
+        f"{'Tip deflection':<20}"
+        f"{deflection_metrics.mae * 1000.0:>12.5f} mm"
+        f"{deflection_metrics.rmse * 1000.0:>12.5f} mm"
+        f"{deflection_metrics.mean_absolute_percentage_error:>14.3f} %"
+    )
+
+    print(
+        f"{'Mode-1 frequency':<20}"
+        f"{frequency_metrics.mae:>12.4f} Hz"
+        f"{frequency_metrics.rmse:>12.4f} Hz"
+        f"{frequency_metrics.mean_absolute_percentage_error:>14.3f} %"
+    )
+
+
+def main() -> None:
+    FIGURE_DIRECTORY.mkdir(
+        parents=True,
+        exist_ok=True,
+    )
+
+    VALIDATION_DIRECTORY.mkdir(
+        parents=True,
+        exist_ok=True,
+    )
+
+    print(f"Loading FEA dataset from {DATASET_PATH}...")
+
+    features, targets = load_surrogate_arrays(DATASET_PATH)
+
+    print(f"Loaded {features.shape[0]} FEA samples.")
+
+    print()
+    print("Training surrogate using deterministic train/validation/test split...")
+
+    training_result = train_surrogate(
+        features,
+        targets,
+        seed=42,
+        batch_size=32,
+        learning_rate=1e-3,
+        maximum_epochs=1000,
+        patience=50,
+    )
+
+    test_indices = training_result.split.test_indices
+
+    test_features = features[test_indices]
+
+    test_targets = targets[test_indices]
+
+    print()
+    print(f"Evaluating {len(test_indices)} held-out test samples...")
+
+    predictions = predict(
+        training_result.model,
+        test_features,
+        feature_standardization=(training_result.feature_standardization),
+        target_standardization=(training_result.target_standardization),
+    )
+
+    stress_reference = test_targets[:, 0]
+
+    stress_prediction = predictions[:, 0]
+
+    deflection_reference = test_targets[:, 1]
+
+    deflection_prediction = predictions[:, 1]
+
+    frequency_reference = test_targets[:, 2]
+
+    frequency_prediction = predictions[:, 2]
+
+    stress_metrics = calculate_metrics(
+        stress_reference,
+        stress_prediction,
+    )
+
+    deflection_metrics = calculate_metrics(
+        deflection_reference,
+        deflection_prediction,
+    )
+
+    frequency_metrics = calculate_metrics(
+        frequency_reference,
+        frequency_prediction,
+    )
+
+    stress_errors_percent = calculate_percentage_errors(
+        stress_reference,
+        stress_prediction,
+    )
+
+    deflection_errors_percent = calculate_percentage_errors(
+        deflection_reference,
+        deflection_prediction,
+    )
+
+    frequency_errors_percent = calculate_percentage_errors(
+        frequency_reference,
+        frequency_prediction,
+    )
+
+    save_loss_plot(training_result)
+
+    save_prediction_scatter(
+        stress_reference / 1e6,
+        stress_prediction / 1e6,
+        x_label="CalculiX FEA stress [MPa]",
+        y_label="PyTorch predicted stress [MPa]",
+        title=("FEA vs PyTorch — Maximum Stress"),
+        output_path=(FIGURE_DIRECTORY / "fea_vs_predicted_stress.png"),
+    )
+
+    save_prediction_scatter(
+        deflection_reference * 1000.0,
+        deflection_prediction * 1000.0,
+        x_label="CalculiX FEA tip deflection [mm]",
+        y_label="PyTorch predicted tip deflection [mm]",
+        title=("FEA vs PyTorch — Tip Deflection"),
+        output_path=(FIGURE_DIRECTORY / "fea_vs_predicted_deflection.png"),
+    )
+
+    save_prediction_scatter(
+        frequency_reference,
+        frequency_prediction,
+        x_label="CalculiX FEA mode-1 frequency [Hz]",
+        y_label="PyTorch predicted mode-1 frequency [Hz]",
+        title=("FEA vs PyTorch — Mode-1 Frequency"),
+        output_path=(FIGURE_DIRECTORY / "fea_vs_predicted_frequency.png"),
+    )
+
+    save_error_distribution(
+        stress_errors_percent,
+        deflection_errors_percent,
+        frequency_errors_percent,
+    )
+
+    save_metrics_csv(
+        stress_metrics=stress_metrics,
+        deflection_metrics=deflection_metrics,
+        frequency_metrics=frequency_metrics,
+    )
+
+    save_test_predictions(
+        reference=test_targets,
+        prediction=predictions,
+        test_indices=test_indices,
+    )
+
+    print_metrics(
+        stress_metrics=stress_metrics,
+        deflection_metrics=deflection_metrics,
+        frequency_metrics=frequency_metrics,
+    )
+
+    print()
+    print(f"Best validation epoch: {training_result.best_epoch}")
+
+    print(f"Best normalized validation MSE: {min(training_result.validation_losses):.6f}")
+
+    print()
+    print("Generated figures:")
+
+    for path in (
+        FIGURE_DIRECTORY / "ml_training_validation_loss.png",
+        FIGURE_DIRECTORY / "fea_vs_predicted_stress.png",
+        FIGURE_DIRECTORY / "fea_vs_predicted_deflection.png",
+        FIGURE_DIRECTORY / "fea_vs_predicted_frequency.png",
+        FIGURE_DIRECTORY / "prediction_error_distribution.png",
+    ):
+        print(f"  {path}")
+
+    print()
+    print("Generated validation data:")
+
+    print("  " + str(VALIDATION_DIRECTORY / "ml_test_metrics.csv"))
+
+    print("  " + str(VALIDATION_DIRECTORY / "ml_test_predictions.csv"))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/generate_surrogate_dataset.py b/scripts/generate_surrogate_dataset.py
new file mode 100644
index 0000000..0f1a6a7
--- /dev/null
+++ b/scripts/generate_surrogate_dataset.py
@@ -0,0 +1,47 @@
+from pathlib import Path
+
+from bodysimpy.config.loader import load_config
+from bodysimpy.ml.dataset_generation import (
+    generate_fea_dataset,
+    write_fea_dataset,
+)
+from bodysimpy.ml.design_space import generate_design_points
+from bodysimpy.modeling.crossmember import build_crossmember_model
+
+SAMPLE_COUNT = 500
+RANDOM_SEED = 42
+MAX_WORKERS = 4
+
+
+def main() -> None:
+    config = load_config("configs/baseline_crossmember.yaml")
+
+    model = build_crossmember_model(config)
+
+    designs = generate_design_points(
+        sample_count=SAMPLE_COUNT,
+        seed=RANDOM_SEED,
+    )
+
+    print(f"Generated {len(designs)} Latin Hypercube design points.")
+
+    samples = generate_fea_dataset(
+        model,
+        designs,
+        max_workers=MAX_WORKERS,
+    )
+
+    output_path = Path("data/surrogate/fea_surrogate_dataset.csv")
+
+    write_fea_dataset(
+        samples,
+        output_path,
+    )
+
+    print()
+    print(f"Saved {len(samples)} FEA samples to:")
+    print(output_path)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/train_surrogate.py b/scripts/train_surrogate.py
new file mode 100644
index 0000000..5093d47
--- /dev/null
+++ b/scripts/train_surrogate.py
@@ -0,0 +1,44 @@
+from pathlib import Path
+
+from bodysimpy.ml.dataset import (
+    load_surrogate_arrays,
+)
+from bodysimpy.ml.training import (
+    save_training_checkpoint,
+    train_surrogate,
+)
+
+
+def main() -> None:
+    dataset_path = Path("data/surrogate/fea_surrogate_dataset.csv")
+
+    features, targets = load_surrogate_arrays(dataset_path)
+
+    result = train_surrogate(
+        features,
+        targets,
+        seed=42,
+        batch_size=32,
+        learning_rate=1e-3,
+        maximum_epochs=1000,
+        patience=50,
+    )
+
+    save_training_checkpoint(
+        result,
+        "models/checkpoints/structural_surrogate.pt",
+    )
+
+    print()
+    print("BodySimPy PyTorch Surrogate")
+    print("=" * 55)
+    print(f"Dataset samples: {features.shape[0]}")
+    print(f"Training samples: {len(result.split.train_indices)}")
+    print(f"Validation samples: {len(result.split.validation_indices)}")
+    print(f"Test samples: {len(result.split.test_indices)}")
+    print(f"Best epoch: {result.best_epoch}")
+    print(f"Final stored validation loss: {min(result.validation_losses):.6f}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/bodysimpy/ml/dataset.py b/src/bodysimpy/ml/dataset.py
new file mode 100644
index 0000000..0ea4bca
--- /dev/null
+++ b/src/bodysimpy/ml/dataset.py
@@ -0,0 +1,115 @@
+from dataclasses import dataclass
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import torch
+from numpy.typing import NDArray
+from torch import Tensor
+from torch.utils.data import Dataset
+
+FEATURE_COLUMNS = (
+    "thickness_m",
+    "height_m",
+    "width_m",
+    "youngs_modulus_pa",
+    "density_kg_m3",
+    "tip_force_n",
+)
+
+TARGET_COLUMNS = (
+    "max_stress_pa",
+    "tip_deflection_m",
+    "mode_1_frequency_hz",
+)
+
+
+@dataclass(frozen=True, slots=True)
+class Standardization:
+    mean: NDArray[np.float64]
+    standard_deviation: NDArray[np.float64]
+
+
+class StructuralSurrogateDataset(Dataset[tuple[Tensor, Tensor]]):
+    """PyTorch dataset for structural surrogate modelling."""
+
+    def __init__(
+        self,
+        features: NDArray[np.float64],
+        targets: NDArray[np.float64],
+    ) -> None:
+        if features.shape[0] != targets.shape[0]:
+            raise ValueError("Feature and target row counts must match.")
+
+        self._features = torch.tensor(
+            features,
+            dtype=torch.float32,
+        )
+
+        self._targets = torch.tensor(
+            targets,
+            dtype=torch.float32,
+        )
+
+    def __len__(self) -> int:
+        return self._features.shape[0]
+
+    def __getitem__(
+        self,
+        index: int,
+    ) -> tuple[Tensor, Tensor]:
+        return (
+            self._features[index],
+            self._targets[index],
+        )
+
+
+def load_surrogate_arrays(
+    path: str | Path,
+) -> tuple[
+    NDArray[np.float64],
+    NDArray[np.float64],
+]:
+    dataframe = pd.read_csv(path)
+
+    features = dataframe[list(FEATURE_COLUMNS)].to_numpy(dtype=np.float64)
+
+    targets = dataframe[list(TARGET_COLUMNS)].to_numpy(dtype=np.float64)
+
+    return features, targets
+
+
+def fit_standardization(
+    values: NDArray[np.float64],
+) -> Standardization:
+    mean = np.mean(
+        values,
+        axis=0,
+    )
+
+    standard_deviation = np.std(
+        values,
+        axis=0,
+    )
+
+    if np.any(standard_deviation == 0.0):
+        raise ValueError("Cannot standardize a constant column.")
+
+    return Standardization(
+        mean=mean,
+        standard_deviation=standard_deviation,
+    )
+
+
+def standardize(
+    values: NDArray[np.float64],
+    statistics: Standardization,
+) -> NDArray[np.float64]:
+    return (values - statistics.mean) / statistics.standard_deviation
+
+
+def inverse_standardize(
+    values: NDArray[np.float64],
+    statistics: Standardization,
+) -> NDArray[np.float64]:
+    return values * statistics.standard_deviation + statistics.mean
diff --git a/src/bodysimpy/ml/dataset_generation.py b/src/bodysimpy/ml/dataset_generation.py
new file mode 100644
index 0000000..a30f8d9
--- /dev/null
+++ b/src/bodysimpy/ml/dataset_generation.py
@@ -0,0 +1,157 @@
+import csv
+from concurrent.futures import ThreadPoolExecutor
+from dataclasses import dataclass, replace
+from pathlib import Path
+
+from bodysimpy.domain.structural_model import StructuralModel
+from bodysimpy.ml.design_space import SurrogateDesignPoint
+from bodysimpy.solvers.calculix import CalculiXSolver
+
+
+@dataclass(frozen=True, slots=True)
+class SurrogateFeaSample:
+    sample_index: int
+
+    thickness_m: float
+    height_m: float
+    width_m: float
+    youngs_modulus_pa: float
+    density_kg_m3: float
+    tip_force_n: float
+
+    max_stress_pa: float
+    tip_deflection_m: float
+    mode_1_frequency_hz: float
+
+
+def evaluate_design_point(
+    base_model: StructuralModel,
+    design: SurrogateDesignPoint,
+) -> SurrogateFeaSample:
+    """Evaluate one surrogate-design point with CalculiX."""
+
+    section = replace(
+        base_model.section,
+        thickness_m=design.thickness_m,
+        height_m=design.height_m,
+        width_m=design.width_m,
+    )
+
+    material = replace(
+        base_model.material,
+        youngs_modulus_pa=design.youngs_modulus_pa,
+        density_kg_m3=design.density_kg_m3,
+    )
+
+    model = replace(
+        base_model,
+        name=(f"{base_model.name}_ml_{design.sample_index:04d}"),
+        section=section,
+        material=material,
+        tip_force_n=design.tip_force_n,
+    )
+
+    solver = CalculiXSolver()
+
+    static_result = solver.run(model)
+
+    modal_result = solver.run_modal(
+        model,
+        modes=1,
+    )
+
+    if static_result.max_axial_stress_pa is None:
+        raise ValueError("Static FEA returned no axial stress.")
+
+    return SurrogateFeaSample(
+        sample_index=design.sample_index,
+        thickness_m=design.thickness_m,
+        height_m=design.height_m,
+        width_m=design.width_m,
+        youngs_modulus_pa=design.youngs_modulus_pa,
+        density_kg_m3=design.density_kg_m3,
+        tip_force_n=design.tip_force_n,
+        max_stress_pa=static_result.max_axial_stress_pa,
+        tip_deflection_m=static_result.tip_deflection_m,
+        mode_1_frequency_hz=(modal_result.natural_frequencies_hz[0]),
+    )
+
+
+def generate_fea_dataset(
+    base_model: StructuralModel,
+    designs: tuple[SurrogateDesignPoint, ...],
+    *,
+    max_workers: int = 4,
+) -> tuple[SurrogateFeaSample, ...]:
+    """Evaluate a complete surrogate training design."""
+
+    if max_workers <= 0:
+        raise ValueError("Maximum worker count must be positive.")
+
+    def evaluate(
+        design: SurrogateDesignPoint,
+    ) -> SurrogateFeaSample:
+        return evaluate_design_point(
+            base_model,
+            design,
+        )
+
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        return tuple(
+            executor.map(
+                evaluate,
+                designs,
+            )
+        )
+
+
+def write_fea_dataset(
+    samples: tuple[SurrogateFeaSample, ...],
+    path: str | Path,
+) -> None:
+    """Write the generated surrogate dataset to CSV."""
+
+    output_path = Path(path)
+
+    output_path.parent.mkdir(
+        parents=True,
+        exist_ok=True,
+    )
+
+    with output_path.open(
+        "w",
+        encoding="utf-8",
+        newline="",
+    ) as stream:
+        writer = csv.writer(stream)
+
+        writer.writerow(
+            [
+                "sample",
+                "thickness_m",
+                "height_m",
+                "width_m",
+                "youngs_modulus_pa",
+                "density_kg_m3",
+                "tip_force_n",
+                "max_stress_pa",
+                "tip_deflection_m",
+                "mode_1_frequency_hz",
+            ]
+        )
+
+        for sample in samples:
+            writer.writerow(
+                [
+                    sample.sample_index,
+                    sample.thickness_m,
+                    sample.height_m,
+                    sample.width_m,
+                    sample.youngs_modulus_pa,
+                    sample.density_kg_m3,
+                    sample.tip_force_n,
+                    sample.max_stress_pa,
+                    sample.tip_deflection_m,
+                    sample.mode_1_frequency_hz,
+                ]
+            )
diff --git a/src/bodysimpy/ml/design_space.py b/src/bodysimpy/ml/design_space.py
new file mode 100644
index 0000000..18a8be7
--- /dev/null
+++ b/src/bodysimpy/ml/design_space.py
@@ -0,0 +1,76 @@
+from dataclasses import dataclass
+
+import numpy as np
+from scipy.stats import qmc
+
+
+@dataclass(frozen=True, slots=True)
+class SurrogateDesignPoint:
+    sample_index: int
+    thickness_m: float
+    height_m: float
+    width_m: float
+    youngs_modulus_pa: float
+    density_kg_m3: float
+    tip_force_n: float
+
+
+def generate_design_points(
+    *,
+    sample_count: int,
+    seed: int,
+) -> tuple[SurrogateDesignPoint, ...]:
+    """Generate a six-dimensional Latin Hypercube design."""
+
+    if sample_count <= 0:
+        raise ValueError("Surrogate sample count must be positive.")
+
+    lower_bounds = np.array(
+        [
+            0.0010,
+            0.032,
+            0.060,
+            190e9,
+            7600.0,
+            700.0,
+        ],
+        dtype=float,
+    )
+
+    upper_bounds = np.array(
+        [
+            0.0020,
+            0.050,
+            0.100,
+            225e9,
+            8100.0,
+            1300.0,
+        ],
+        dtype=float,
+    )
+
+    sampler = qmc.LatinHypercube(
+        d=6,
+        seed=seed,
+    )
+
+    unit_sample = sampler.random(n=sample_count)
+
+    scaled_sample = qmc.scale(
+        unit_sample,
+        lower_bounds,
+        upper_bounds,
+    )
+
+    return tuple(
+        SurrogateDesignPoint(
+            sample_index=index + 1,
+            thickness_m=float(row[0]),
+            height_m=float(row[1]),
+            width_m=float(row[2]),
+            youngs_modulus_pa=float(row[3]),
+            density_kg_m3=float(row[4]),
+            tip_force_n=float(row[5]),
+        )
+        for index, row in enumerate(scaled_sample)
+    )
diff --git a/src/bodysimpy/ml/evaluation.py b/src/bodysimpy/ml/evaluation.py
new file mode 100644
index 0000000..9970285
--- /dev/null
+++ b/src/bodysimpy/ml/evaluation.py
@@ -0,0 +1,96 @@
+from dataclasses import dataclass
+
+import numpy as np
+import torch
+from numpy.typing import NDArray
+
+from bodysimpy.ml.dataset import (
+    Standardization,
+    inverse_standardize,
+    standardize,
+)
+from bodysimpy.ml.model import StructuralSurrogate
+
+
+@dataclass(frozen=True, slots=True)
+class TargetMetrics:
+    """Regression metrics for one structural response."""
+
+    mae: float
+    rmse: float
+    mean_absolute_percentage_error: float
+
+
+def predict(
+    model: StructuralSurrogate,
+    features: NDArray[np.float64],
+    *,
+    feature_standardization: Standardization,
+    target_standardization: Standardization,
+) -> NDArray[np.float64]:
+    """Predict structural responses in physical units."""
+
+    normalized_features = standardize(
+        features,
+        feature_standardization,
+    )
+
+    feature_tensor = torch.tensor(
+        normalized_features,
+        dtype=torch.float32,
+    )
+
+    model.eval()
+
+    with torch.no_grad():
+        normalized_predictions = model(feature_tensor).cpu().numpy().astype(np.float64)
+
+    return inverse_standardize(
+        normalized_predictions,
+        target_standardization,
+    )
+
+
+def calculate_metrics(
+    reference: NDArray[np.float64],
+    prediction: NDArray[np.float64],
+) -> TargetMetrics:
+    """Calculate MAE, RMSE and MAPE."""
+
+    if reference.shape != prediction.shape:
+        raise ValueError("Reference and prediction arrays must have matching shapes.")
+
+    if reference.size == 0:
+        raise ValueError("At least one prediction is required.")
+
+    if np.any(reference == 0.0):
+        raise ValueError("MAPE cannot be calculated with zero reference values.")
+
+    errors = prediction - reference
+
+    mae = float(np.mean(np.abs(errors)))
+
+    rmse = float(np.sqrt(np.mean(errors**2)))
+
+    percentage_errors = np.abs(errors) / np.abs(reference) * 100.0
+
+    return TargetMetrics(
+        mae=mae,
+        rmse=rmse,
+        mean_absolute_percentage_error=float(np.mean(percentage_errors)),
+    )
+
+
+def calculate_percentage_errors(
+    reference: NDArray[np.float64],
+    prediction: NDArray[np.float64],
+) -> NDArray[np.float64]:
+    """Return absolute percentage error for each prediction."""
+
+    if reference.shape != prediction.shape:
+        raise ValueError("Reference and prediction arrays must have matching shapes.")
+
+    if np.any(reference == 0.0):
+        raise ValueError("Percentage error cannot use zero reference values.")
+
+    return np.abs(prediction - reference) / np.abs(reference) * 100.0
diff --git a/src/bodysimpy/ml/model.py b/src/bodysimpy/ml/model.py
new file mode 100644
index 0000000..8e8dba6
--- /dev/null
+++ b/src/bodysimpy/ml/model.py
@@ -0,0 +1,29 @@
+from typing import cast
+
+from torch import Tensor, nn
+
+
+class StructuralSurrogate(nn.Module):
+    """MLP surrogate for structural FEA responses."""
+
+    def __init__(self) -> None:
+        super().__init__()
+
+        self.network = nn.Sequential(
+            nn.Linear(6, 64),
+            nn.ReLU(),
+            nn.Linear(64, 64),
+            nn.ReLU(),
+            nn.Linear(64, 32),
+            nn.ReLU(),
+            nn.Linear(32, 3),
+        )
+
+    def forward(
+        self,
+        features: Tensor,
+    ) -> Tensor:
+        return cast(
+            Tensor,
+            self.network(features),
+        )
\ No newline at end of file
diff --git a/src/bodysimpy/ml/training.py b/src/bodysimpy/ml/training.py
new file mode 100644
index 0000000..818322c
--- /dev/null
+++ b/src/bodysimpy/ml/training.py
@@ -0,0 +1,261 @@
+from copy import deepcopy
+from dataclasses import dataclass
+from pathlib import Path
+
+import numpy as np
+import torch
+from numpy.typing import NDArray
+from torch import Tensor, nn
+from torch.optim import Adam
+from torch.utils.data import DataLoader
+
+from bodysimpy.ml.dataset import (
+    Standardization,
+    StructuralSurrogateDataset,
+    fit_standardization,
+    standardize,
+)
+from bodysimpy.ml.model import StructuralSurrogate
+
+
+@dataclass(frozen=True, slots=True)
+class DataSplit:
+    train_indices: NDArray[np.int64]
+    validation_indices: NDArray[np.int64]
+    test_indices: NDArray[np.int64]
+
+
+@dataclass(frozen=True, slots=True)
+class TrainingResult:
+    model: StructuralSurrogate
+    feature_standardization: Standardization
+    target_standardization: Standardization
+    train_losses: tuple[float, ...]
+    validation_losses: tuple[float, ...]
+    best_epoch: int
+    split: DataSplit
+
+
+def create_data_split(
+    sample_count: int,
+    *,
+    seed: int,
+) -> DataSplit:
+    if sample_count < 10:
+        raise ValueError("At least ten samples are required.")
+
+    rng = np.random.default_rng(seed)
+
+    indices = rng.permutation(sample_count)
+
+    train_end = int(0.70 * sample_count)
+
+    validation_end = int(0.85 * sample_count)
+
+    return DataSplit(
+        train_indices=indices[:train_end],
+        validation_indices=indices[train_end:validation_end],
+        test_indices=indices[validation_end:],
+    )
+
+
+def _mean_loss(
+    model: StructuralSurrogate,
+    loader: DataLoader[tuple[Tensor, Tensor]],
+    loss_function: nn.Module,
+    device: torch.device,
+) -> float:
+    model.eval()
+
+    losses: list[float] = []
+
+    with torch.no_grad():
+        for features, targets in loader:
+            features = features.to(device)
+            targets = targets.to(device)
+
+            predictions = model(features)
+
+            loss = loss_function(
+                predictions,
+                targets,
+            )
+
+            losses.append(float(loss.item()))
+
+    return float(np.mean(losses))
+
+
+def train_surrogate(
+    features: NDArray[np.float64],
+    targets: NDArray[np.float64],
+    *,
+    seed: int = 42,
+    batch_size: int = 32,
+    learning_rate: float = 1e-3,
+    maximum_epochs: int = 1000,
+    patience: int = 50,
+) -> TrainingResult:
+    """Train the BodySimPy structural surrogate."""
+
+    torch.manual_seed(seed)
+
+    split = create_data_split(
+        features.shape[0],
+        seed=seed,
+    )
+
+    train_features = features[split.train_indices]
+
+    train_targets = targets[split.train_indices]
+
+    feature_statistics = fit_standardization(train_features)
+
+    target_statistics = fit_standardization(train_targets)
+
+    normalized_features = standardize(
+        features,
+        feature_statistics,
+    )
+
+    normalized_targets = standardize(
+        targets,
+        target_statistics,
+    )
+
+    train_dataset = StructuralSurrogateDataset(
+        normalized_features[split.train_indices],
+        normalized_targets[split.train_indices],
+    )
+
+    validation_dataset = StructuralSurrogateDataset(
+        normalized_features[split.validation_indices],
+        normalized_targets[split.validation_indices],
+    )
+
+    training_loader = DataLoader(
+        train_dataset,
+        batch_size=batch_size,
+        shuffle=True,
+    )
+
+    validation_loader = DataLoader(
+        validation_dataset,
+        batch_size=batch_size,
+        shuffle=False,
+    )
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    model = StructuralSurrogate().to(device)
+
+    optimizer = Adam(
+        model.parameters(),
+        lr=learning_rate,
+    )
+
+    loss_function = nn.MSELoss()
+
+    train_losses: list[float] = []
+    validation_losses: list[float] = []
+
+    best_validation_loss = float("inf")
+    best_model_state = deepcopy(model.state_dict())
+
+    best_epoch = 0
+    epochs_without_improvement = 0
+
+    for epoch in range(
+        1,
+        maximum_epochs + 1,
+    ):
+        model.train()
+
+        batch_losses: list[float] = []
+
+        for features_batch, targets_batch in training_loader:
+            features_batch = features_batch.to(device)
+
+            targets_batch = targets_batch.to(device)
+
+            optimizer.zero_grad()
+
+            predictions = model(features_batch)
+
+            loss = loss_function(
+                predictions,
+                targets_batch,
+            )
+
+            loss.backward()
+
+            optimizer.step()
+
+            batch_losses.append(float(loss.item()))
+
+        training_loss = float(np.mean(batch_losses))
+
+        validation_loss = _mean_loss(
+            model,
+            validation_loader,
+            loss_function,
+            device,
+        )
+
+        train_losses.append(training_loss)
+
+        validation_losses.append(validation_loss)
+
+        if validation_loss < best_validation_loss:
+            best_validation_loss = validation_loss
+
+            best_model_state = deepcopy(model.state_dict())
+
+            best_epoch = epoch
+            epochs_without_improvement = 0
+        else:
+            epochs_without_improvement += 1
+
+        if epochs_without_improvement >= patience:
+            break
+
+    model.load_state_dict(best_model_state)
+
+    model.to("cpu")
+    model.eval()
+
+    return TrainingResult(
+        model=model,
+        feature_standardization=(feature_statistics),
+        target_standardization=(target_statistics),
+        train_losses=tuple(train_losses),
+        validation_losses=tuple(validation_losses),
+        best_epoch=best_epoch,
+        split=split,
+    )
+
+
+def save_training_checkpoint(
+    result: TrainingResult,
+    path: str | Path,
+) -> None:
+    """Save trained model and preprocessing metadata."""
+
+    checkpoint_path = Path(path)
+
+    checkpoint_path.parent.mkdir(
+        parents=True,
+        exist_ok=True,
+    )
+
+    torch.save(
+        {
+            "model_state_dict": (result.model.state_dict()),
+            "feature_mean": (result.feature_standardization.mean),
+            "feature_standard_deviation": (result.feature_standardization.standard_deviation),
+            "target_mean": (result.target_standardization.mean),
+            "target_standard_deviation": (result.target_standardization.standard_deviation),
+            "best_epoch": (result.best_epoch),
+        },
+        checkpoint_path,
+    )
diff --git a/tests/integration/test_ml_dataset_generation.py b/tests/integration/test_ml_dataset_generation.py
new file mode 100644
index 0000000..983a81a
--- /dev/null
+++ b/tests/integration/test_ml_dataset_generation.py
@@ -0,0 +1,36 @@
+import shutil
+
+import pytest
+
+from bodysimpy.config.loader import load_config
+from bodysimpy.ml.dataset_generation import generate_fea_dataset
+from bodysimpy.ml.design_space import generate_design_points
+from bodysimpy.modeling.crossmember import build_crossmember_model
+
+
+@pytest.mark.skipif(
+    shutil.which("ccx") is None,
+    reason="CalculiX is not installed.",
+)
+def test_generate_small_fea_surrogate_dataset() -> None:
+    config = load_config("configs/baseline_crossmember.yaml")
+
+    model = build_crossmember_model(config)
+
+    designs = generate_design_points(
+        sample_count=2,
+        seed=42,
+    )
+
+    samples = generate_fea_dataset(
+        model,
+        designs,
+        max_workers=2,
+    )
+
+    assert len(samples) == 2
+
+    for sample in samples:
+        assert sample.max_stress_pa > 0.0
+        assert sample.tip_deflection_m > 0.0
+        assert sample.mode_1_frequency_hz > 0.0
diff --git a/tests/unit/test_ml_dataset.py b/tests/unit/test_ml_dataset.py
new file mode 100644
index 0000000..ff6297a
--- /dev/null
+++ b/tests/unit/test_ml_dataset.py
@@ -0,0 +1,57 @@
+import numpy as np
+import pytest
+
+from bodysimpy.ml.dataset import (
+    StructuralSurrogateDataset,
+    fit_standardization,
+    inverse_standardize,
+    standardize,
+)
+
+
+def test_surrogate_dataset_shapes() -> None:
+    features = np.ones(
+        (10, 6),
+        dtype=np.float64,
+    )
+
+    targets = np.ones(
+        (10, 3),
+        dtype=np.float64,
+    )
+
+    dataset = StructuralSurrogateDataset(
+        features,
+        targets,
+    )
+
+    features_row, targets_row = dataset[0]
+
+    assert len(dataset) == 10
+    assert tuple(features_row.shape) == (6,)
+    assert tuple(targets_row.shape) == (3,)
+
+
+def test_standardization_round_trip() -> None:
+    values = np.array(
+        [
+            [1.0, 10.0],
+            [2.0, 20.0],
+            [3.0, 30.0],
+        ],
+        dtype=np.float64,
+    )
+
+    statistics = fit_standardization(values)
+
+    normalized = standardize(
+        values,
+        statistics,
+    )
+
+    recovered = inverse_standardize(
+        normalized,
+        statistics,
+    )
+
+    assert recovered == pytest.approx(values)
diff --git a/tests/unit/test_ml_design_space.py b/tests/unit/test_ml_design_space.py
new file mode 100644
index 0000000..3bb9ddf
--- /dev/null
+++ b/tests/unit/test_ml_design_space.py
@@ -0,0 +1,42 @@
+import pytest
+
+from bodysimpy.ml.design_space import generate_design_points
+
+
+def test_generate_design_points() -> None:
+    points = generate_design_points(
+        sample_count=10,
+        seed=42,
+    )
+
+    assert len(points) == 10
+
+    for point in points:
+        assert 0.0010 <= point.thickness_m <= 0.0020
+        assert 0.032 <= point.height_m <= 0.050
+        assert 0.060 <= point.width_m <= 0.100
+        assert 190e9 <= point.youngs_modulus_pa <= 225e9
+        assert 7600.0 <= point.density_kg_m3 <= 8100.0
+        assert 700.0 <= point.tip_force_n <= 1300.0
+
+
+def test_design_generation_is_reproducible() -> None:
+    first = generate_design_points(
+        sample_count=5,
+        seed=42,
+    )
+
+    second = generate_design_points(
+        sample_count=5,
+        seed=42,
+    )
+
+    assert first == second
+
+
+def test_design_generation_rejects_invalid_sample_count() -> None:
+    with pytest.raises(ValueError):
+        generate_design_points(
+            sample_count=0,
+            seed=42,
+        )
diff --git a/tests/unit/test_ml_model.py b/tests/unit/test_ml_model.py
new file mode 100644
index 0000000..0d6c556
--- /dev/null
+++ b/tests/unit/test_ml_model.py
@@ -0,0 +1,19 @@
+import torch
+
+from bodysimpy.ml.model import StructuralSurrogate
+
+
+def test_surrogate_model_output_shape() -> None:
+    model = StructuralSurrogate()
+
+    batch = torch.randn(
+        8,
+        6,
+    )
+
+    output = model(batch)
+
+    assert tuple(output.shape) == (
+        8,
+        3,
+    )