Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915102076174336 |
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| author | Abdelrehim, Anas Gandhi, Dhairya Yalburgi, Sharan Bharambe, Ashutosh Anantharaman, Ranjan Rackauckas, Chris |
| author_facet | Abdelrehim, Anas Gandhi, Dhairya Yalburgi, Sharan Bharambe, Ashutosh Anantharaman, Ranjan Rackauckas, Chris |
| contents | Surrogate models are effective tools for accelerated design of complex systems. The result of a design optimization procedure using surrogate models can be used to initialize an optimization routine using the full order system. High accuracy of the surrogate model can be advantageous for fast convergence. In this work, we present an active learning approach to produce a very high accuracy surrogate model of a turbofan jet engine, that demonstrates 0.1\% relative error for all quantities of interest. We contrast this with a surrogate model produced using a more traditional brute-force data generation approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07701 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Abdelrehim, Anas Gandhi, Dhairya Yalburgi, Sharan Bharambe, Ashutosh Anantharaman, Ranjan Rackauckas, Chris Computational Engineering, Finance, and Science Surrogate models are effective tools for accelerated design of complex systems. The result of a design optimization procedure using surrogate models can be used to initialize an optimization routine using the full order system. High accuracy of the surrogate model can be advantageous for fast convergence. In this work, we present an active learning approach to produce a very high accuracy surrogate model of a turbofan jet engine, that demonstrates 0.1\% relative error for all quantities of interest. We contrast this with a surrogate model produced using a more traditional brute-force data generation approach. |
| title | Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2501.07701 |