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config.py

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+"""Global configuration for the Physics-Informed Bayesian Optimization platform."""
+
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Optional
+
+
+class OptimizerBackend(Enum):
+    BOTORCH = "botorch"
+    AX = "ax"
+    BOFIRE = "bofire"
+
+
+class AcquisitionType(Enum):
+    EXPECTED_IMPROVEMENT = "EI"
+    UPPER_CONFIDENCE_BOUND = "UCB"
+    PROBABILITY_OF_IMPROVEMENT = "PI"
+    KNOWLEDGE_GRADIENT = "KG"
+    NOISY_EXPECTED_IMPROVEMENT = "NEI"
+    PHYSICS_INFORMED_EI = "PI_EI"  # Custom: penalizes physically implausible regions
+
+
+@dataclass
+class OptimizationConfig:
+    """Configuration for a Bayesian optimization run."""
+
+    # Backend selection
+    backend: OptimizerBackend = OptimizerBackend.BOTORCH
+
+    # Acquisition function
+    acquisition_type: AcquisitionType = AcquisitionType.EXPECTED_IMPROVEMENT
+
+    # Optimization settings
+    n_initial_samples: int = 10
+    batch_size: int = 1
+    max_iterations: int = 50
+    seed: int = 42
+
+    # GP settings
+    use_physics_mean: bool = True
+    learn_noise: bool = True
+    noise_variance: float = 0.01
+
+    # Physics model settings
+    physics_model_weight: float = 1.0  # Weight of physics prior (0=pure GP, 1=full physics)
+    physics_constraint_penalty: float = 10.0
+
+    # Multi-fidelity settings
+    use_multi_fidelity: bool = False
+    fidelity_weights: Optional[dict] = None
+
+    # Hardware
+    device: str = "cpu"
+    dtype: str = "float64"
+
+
+@dataclass
+class SearchSpaceConfig:
+    """Configuration for the search/parameter space."""
+
+    normalize_inputs: bool = True
+    standardize_outputs: bool = True
+    input_transform: Optional[str] = None
+    output_transform: Optional[str] = None