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MF-GLaM: A multifidelity stochastic emulator using generalized lambda models
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DOI：10.1016/j.cma.2025.118498.png)
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Abstract
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• MF-GLaM is a novel multifidelity approach for emulating the full conditional response distribution of high-fidelity stochastic simulators at each input. • It is non-intrusive, requiring no access to the internal stochasticity of the simulators nor repeated evaluations at the same input values. • It extends the recently published generalized lambda models by fusing the parameters from both fidelities via polynomial chaos expansions. • It jointly fits its parameters across fidelities via maximum-likelihood estimation and an automated basis-selection strategy. • MF-GLaM achieves faster convergence and significant computational savings over single-fidelity emulators.
Keywords:
Multifidelity modeling
Stochastic simulators
Stochastic emulators
Surrogate models
Generalized lambda models
Polynomial chaos expansions
65C60
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