"""
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",
]