Multifidelity simulation

Multifidelity 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, wing-design optimization, robotic learning, computational biomechanics, and have more recently been extended to human-in-the-loop systems, such as aerospace and transportation. They include both model-based methods, where a generative model is available or can be learned, in addition to model-free methods, that include regression-based approaches, such as stacked-regression. A more general class of regression-based multi-fidelity methods are Bayesian approaches, e.g. Bayesian linear regression, Gaussian mixture models, Gaussian processes, auto-regressive Gaussian processes, or Bayesian polynomial chaos expansions.

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