Extending multi-fidelity surrogate modelling to stochastic simulators
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2024-02
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Other Conference Item
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Abstract
Multi-fidelity surrogate models (MFSMs) fuse information from models with varying computational fidelities into a new surrogate model (SM). Such models can predict the output of complex systems with a higher accuracy and lower cost compared to single-fidelity SMs. MFSMs can be constructed based on high- and lower-fidelity data from physical experiments and/or computer simulations.
In real-world applications, experiments are often contaminated by measurement noise, while computer simulations may be polluted by numerical noise. This noise introduces a non-deterministic element, rendering the response of a model or experiment a random variable, even when the input parameters are fixed.
This non-deterministic model behaviour is typical of stochastic simulators. These simulators are pertinent when latent sources of uncertainty impact a system, resulting in the response being a random variable conditioned on the input parameters. Stochastic simulators can effectively capture the noisy nature of both high- and lower-fidelity models.
To alleviate the cost of repeatedly evaluating stochastic simulators, different methods exist to emulate their response. Such approaches can be statistical, replication-, or spectral expansions-based.
This work explores the synergy between MFSMs and stochastic simulators, paving the way for the development of multi-fidelity stochastic emulators.
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published
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ETH Zurich, Risk, Safety and Uncertainty Quantification
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SIAM Conference on Uncertainty Quantification (UQ 2024)
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03962 - Sudret, Bruno / Sudret, Bruno
Notes
Conference lecture held on February 28, 2024.
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955393 - European Training Network on Grey-Box Models for Safe and Reliable Intelligent Mobility Systems (EC)