File size: 1,211 Bytes
495f8fd | 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 | """
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",
]
|