An adaptive multi-fidelity optimization framework based on co-Kriging surrogate models and stochastic sampling with application to coastal aquifer management

Christelis, Vasileios; Kopsiaftis, George; Regis, Rommel G.; Mantoglou, Aristotelis. 2023 An adaptive multi-fidelity optimization framework based on co-Kriging surrogate models and stochastic sampling with application to coastal aquifer management. Advances in Water Resources, 180, 104537. https://doi.org/10.1016/j.advwatres.2023.104537

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Official URL: http://dx.doi.org/10.1016/j.advwatres.2023.104537

Abstract/Summary

Surrogate modelling has been used successfully to alleviate the computational burden that results from high-fidelity numerical models of seawater intrusion in simulation-optimization routines. Nevertheless, little attention has been given to multi-fidelity modelling methods to address cases where only limited runs with computationally expensive seawater intrusion models are considered affordable imposing a limiting factor for single-fidelity surrogate-based optimization as well. In this work, a new adaptive multi-fidelity optimization framework is proposed based on co-Kriging surrogate models considering two model fidelities of seawater intrusion. The methodology is tailored to the needs of solving pumping optimization problems with computationally expensive constraint functions and utilizes only small high-fidelity training datasets. Results from both hypothetical and real-world optimization problems demonstrate the efficiency and practicality of the proposed framework to provide a steep improvement of the objective function while it outperforms a comprehensive single-fidelity surrogate-based optimization method. The method can also be used to locate optimal solutions in the region of the global optimum when larger high-fidelity training datasets are available.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.advwatres.2023.104537
ISSN:	03091708
Additional Keywords:	Groundwater, GroundwaterBGS
Date made live:	20 Sep 2023 14:46 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/535863

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.advwatres.2023.104537

ISSN:

03091708

Additional Keywords:

Groundwater, GroundwaterBGS

Date made live:

20 Sep 2023 14:46 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/535863