Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling
Strategy
Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling
Strategy
Surrogate models are often used as computationally efficient approximations to complex simulation models, enabling tasks such as solving inverse problems, sensitivity analysis, and probabilistic forward predictions, which would otherwise be computationally infeasible. During training, surrogate parameters are fitted such that the surrogate reproduces the simulation model's outputs as closely as …