Surrogates

Surrogate models are used to model and estimate the unknown objective function of the DoE campaign. BayBE offers a diverse array of surrogate models, while also allowing for the utilization of custom models. All surrogate models are based upon the general Surrogate class. Some models even support transfer learning, as indicated by the supports_transfer_learning attribute.

Available models

BayBE provides a comprehensive selection of surrogate models, empowering you to choose the most suitable option for your specific needs. The following surrogate models are available within BayBE:

• GaussianProcessSurrogate

• BayesianLinearSurrogate

• MeanPredictionSurrogate

• NGBoostSurrogate

• RandomForestSurrogate

Using custom models

BayBE goes one step further by allowing you to incorporate custom models based on the ONNX architecture. Note however that these cannot be retrained. For a detailed explanation on using custom models, refer to the comprehensive examples provided in the corresponding example folder.