Multifidelity importance sampling

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Optimal Model Management for Multifidelity Monte Carlo Estimation (2016, methodology): “Another body of work combines the high-fidelity model with a surrogate model in the context of the Monte Carlo method with importance sampling [31, 30, 13, 41, 38].”
Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018, methodology): “2 Construction of the biasing distribution with low-fidelity models The multifidelity importance sampling approach introduced in [159] uses a low-fidelity model to construct the biasing distribution in the first step of importance sampling, and derives the statistics using high-fidelity model evalua”
Review of multi-fidelity models (2016, methodology): “Nonetheless, it is mainly beneficial for eliminating models that perform poorly [165, 169].”
Rare event estimation with sequential directional importance sampling (2022, methodology): “Numerical simulation methods include Monte Carlo simulation (MCS) and variance reduction methods such as importance sampling (IS) [18-20] and its enhanced versions [21-24], subset simulation (SuS) [15, 16, 25], thermodynamic integration and parallel tempering (TIPT) [26], line sampling (LS) [27-29],”