Multifidelity (or multi-fidelity) methods leverage both low- and high-fidelity data in order to maximize the accuracy of model estimates, while minimizing the cost associated with parametrization.

They have been successfully used in impedance cardiography,[2][3][4] wing-design optimization,[5] robotic learning,[6] computational biomechanics,[7] and have more recently been extended to human-in-the-loop systems, such as aerospace[8] and transportation.

[8] A more general class of regression-based multi-fidelity methods are Bayesian approaches, e.g. Bayesian linear regression,[3] Gaussian mixture models,[10][11] Gaussian processes,[12] auto-regressive Gaussian processes,[2] or Bayesian polynomial chaos expansions.

[4] The approach used depends on the domain and properties of the data available, and is similar to the concept of metasynthesis, proposed by Judea Pearl.

[13] The fidelity of data can vary along a spectrum between low- and high-fidelity.

[5] Moreover, in human-in-the-loop (HITL) situations the goal may be to predict the impact of technology on expert behavior within the real-world operational context.

Machine learning can be used to train statistical models that predict expert behavior, provided that an adequate amount of high-fidelity (i.e., real-world) data are available or can be produced.

For example, low-fidelity data can be acquired by using a distributed simulation platform, such as X-Plane, and requiring novice participants to operate in scenarios that are approximations of the real-world context.

However, the limitation is that the low-fidelity data may not be useful for predicting real-world expert (i.e., high-fidelity) performance due to differences between the low-fidelity simulation platform and the real-world context, or between novice and expert performance (e.g., due to training).

[8][9] High-fidelity data (HiFi) includes data that was produced by a person or Stochastic Process that closely matches the operational context of interest.

For example, in wing design optimization, high-fidelity data uses physical models in simulation that produce results that closely match the wing in a similar real-world setting.

[9] An obvious benefit of utilizing high-fidelity data is that the estimates produced by the model should generalize well to the real-world context.

The limited amount of data available can significantly impair the ability of the model to produce valid estimates.

[8] Multifidelity methods attempt to leverage the strengths of each data source, while overcoming the limitations.

Although small to medium differences between low- and high-fidelity data are sometimes able to be overcome by multifidelity models, large differences (e.g., in KL divergence between novice and expert action distributions) can be problematic leading to decreased predictive performance when compared to models that exclusively relied on high-fidelity data.

Gaussian processes (GP), each level of output fidelity,

[16][17] Under the assumption, that all information about a level is contained in the data corresponding to the same pivot point