Abstract
The design space is defined as the combination of materials and process conditions which provides assurance of quality. Identification of the design space is a computationally demanding task especially in high dimensional settings. The active subspaces method is a technique that identifies the most important directions in the parameter space, enabling significant dimension reduction. We show how to apply the active subspaces method for model reductions and identification of design space. The results of constraint global sensitivity analysis match those obtained with the active subspaces method for the considered test case.
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Acknowledgements
We acknowledge the financial support of Eli Lilly and Company and the EPSRC Programme Grant EP/T005556/1.
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Kucherenko, S., Shah, N., Zaccheus, O. (2024). Application of Active Subspaces for Model Reduction and Identification of Design Space. In: Lirkov, I., Margenov, S. (eds) Large-Scale Scientific Computations. LSSC 2023. Lecture Notes in Computer Science, vol 13952. Springer, Cham. https://doi.org/10.1007/978-3-031-56208-2_42
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DOI: https://doi.org/10.1007/978-3-031-56208-2_42
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