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__init__.py

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+"""
+Physics-Informed Bayesian Optimization Platform (PIBO)
+
+A platform for designing experiments using physics-informed surrogate models
+with Bayesian optimization. Supports BoTorch, AX, GPyTorch, and BoFire backends.
+
+Core concept: Use physical models as structured priors (mean functions) for
+Gaussian Processes, so the GP learns residuals between physics predictions
+and real observations. This dramatically improves sample efficiency.
+"""
+
+from physics_informed_bo.models.base import SurrogateModel
+from physics_informed_bo.models.physics_model import PhysicsModel, PhysicsMeanFunction
+from physics_informed_bo.models.hybrid_model import HybridSurrogate
+from physics_informed_bo.experiment.designer import ExperimentDesigner
+from physics_informed_bo.experiment.parameter_space import ParameterSpace, ContinuousParameter, CategoricalParameter
+from physics_informed_bo.experiment.campaign import OptimizationCampaign
+
+__version__ = "0.1.0"
+
+__all__ = [
+    "SurrogateModel",
+    "PhysicsModel",
+    "PhysicsMeanFunction",
+    "HybridSurrogate",
+    "ExperimentDesigner",
+    "ParameterSpace",
+    "ContinuousParameter",
+    "CategoricalParameter",
+    "OptimizationCampaign",
+]