Stochastic emulators (generalized lambda models and stochastic PCE) approximate conditional response distributions to break the double-loop structure in RBDO and deliver computational gains in high dimensions over Kriging.
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MF-GLaM extends generalized lambda models into a multifidelity framework to emulate conditional response distributions of stochastic simulators, achieving better accuracy at equal cost or comparable accuracy at lower cost.
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High-dimensional reliability-based design optimization using stochastic emulators
Stochastic emulators (generalized lambda models and stochastic PCE) approximate conditional response distributions to break the double-loop structure in RBDO and deliver computational gains in high dimensions over Kriging.
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MF-GLaM: A multifidelity stochastic emulator using generalized lambda models
MF-GLaM extends generalized lambda models into a multifidelity framework to emulate conditional response distributions of stochastic simulators, achieving better accuracy at equal cost or comparable accuracy at lower cost.