LAPSE:2025.0393
Published Article
LAPSE:2025.0393
Design Space Exploration via Gaussian Process Regression and Alpha Shape Visualization
June 27, 2025
Abstract
This study introduces a novel methodology that combines Gaussian process regression (GPR) with alpha shape design space reconstruction to visualize multi-dimensional design spaces. The proposed GPR surrogate approach incorporates a kernel optimization step, employing a greedy tree search strategy to identify the optimal combinatorial kernel from a selection of base kernels. This approach efficiently evaluates design spaces around specific points of interest, enabling alpha shape reconstruction. The methodology's adaptability is demonstrated through its application to both lower-dimensional (2D and 3D) cases and more complex, higher-dimensional systems (up to 7D), showcasing its scalability and versatility. Its effectiveness is further validated by its ability to generate accurate surrogate models from limited data. Overall, this study presents a robust framework that leverages GPR surrogate modeling and alpha shape reconstruction to facilitate design space evaluation in complex, multidimensional engineering problems.
This study introduces a novel methodology that combines Gaussian process regression (GPR) with alpha shape design space reconstruction to visualize multi-dimensional design spaces. The proposed GPR surrogate approach incorporates a kernel optimization step, employing a greedy tree search strategy to identify the optimal combinatorial kernel from a selection of base kernels. This approach efficiently evaluates design spaces around specific points of interest, enabling alpha shape reconstruction. The methodology's adaptability is demonstrated through its application to both lower-dimensional (2D and 3D) cases and more complex, higher-dimensional systems (up to 7D), showcasing its scalability and versatility. Its effectiveness is further validated by its ability to generate accurate surrogate models from limited data. Overall, this study presents a robust framework that leverages GPR surrogate modeling and alpha shape reconstruction to facilitate design space evaluation in complex, multidimensional engineering problems.
Record ID
Keywords
Alpha Shapes, Design Space Identification, Gaussian Process Regression, Kernel Optimisation, Surrogate Modelling
Subject
Suggested Citation
Marich E, Galeazzi A, Sachio S, Michalopoulou F, Papathanasiou MM. Design Space Exploration via Gaussian Process Regression and Alpha Shape Visualization. Systems and Control Transactions 4:1498-1504 (2025) https://doi.org/10.69997/sct.192990
Author Affiliations
Marich E: Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK; The Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
Galeazzi A: Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK; The Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
Sachio S: Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK; The Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
Michalopoulou F: Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK; The Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
Papathanasiou MM: Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK; The Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
Galeazzi A: Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK; The Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
Sachio S: Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK; The Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
Michalopoulou F: Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK; The Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
Papathanasiou MM: Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK; The Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
Journal Name
Systems and Control Transactions
Volume
4
First Page
1498
Last Page
1504
Year
2025
Publication Date
2025-07-01
Version Comments
Original Submission
Other Meta
PII: 1498-1504-1505-SCT-4-2025, Publication Type: Journal Article
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LAPSE:2025.0393
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https://doi.org/10.69997/sct.192990
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Jun 27, 2025
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References Cited
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