There’s an interest from the community (e.g., BayBE) in being able to perform transfer learning within a Bayesian optimization framework to avoid the “cold-start” characteristic of many Bayesian optimization campaigns despite related data being available. Some examples:

Maximizing chemical reaction yields based on a related chemical reaction yield that shares a subset of parameters

Maximizing measured superhardness while incorporating physics-based simulations of bulk and shear moduli simulations (related parameters, search space between simulation and experiment is not one-to-one)

Maximizing experimentally observed yield strength and elongation at fracture while incorporating physics-based simulations (e.g., Thermo-Calc)

Some questions come up, such as:

What are effective ways to approach benchmarking with this topic?

What are the state-of-the-art (already implemented) methods for doing so?

Astudillo, R.; Frazier, P. Bayesian Optimization of Composite Functions. In Proceedings of the 36th International Conference on Machine Learning; PMLR, 2019; pp 354–363.