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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.20886 |
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| _version_ | 1866917842012602368 |
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| author | Janssen, Robin Sulzer, Immanuel Buck, Tobias |
| author_facet | Janssen, Robin Sulzer, Immanuel Buck, Tobias |
| contents | We introduce CODES, a benchmark for comprehensive evaluation of surrogate architectures for coupled ODE systems. Besides standard metrics like mean squared error (MSE) and inference time, CODES provides insights into surrogate behaviour across multiple dimensions like interpolation, extrapolation, sparse data, uncertainty quantification and gradient correlation. The benchmark emphasizes usability through features such as integrated parallel training, a web-based configuration generator, and pre-implemented baseline models and datasets. Extensive documentation ensures sustainability and provides the foundation for collaborative improvement. By offering a fair and multi-faceted comparison, CODES helps researchers select the most suitable surrogate for their specific dataset and application while deepening our understanding of surrogate learning behaviour. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20886 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CODES: Benchmarking Coupled ODE Surrogates Janssen, Robin Sulzer, Immanuel Buck, Tobias Machine Learning Instrumentation and Methods for Astrophysics Computational Physics We introduce CODES, a benchmark for comprehensive evaluation of surrogate architectures for coupled ODE systems. Besides standard metrics like mean squared error (MSE) and inference time, CODES provides insights into surrogate behaviour across multiple dimensions like interpolation, extrapolation, sparse data, uncertainty quantification and gradient correlation. The benchmark emphasizes usability through features such as integrated parallel training, a web-based configuration generator, and pre-implemented baseline models and datasets. Extensive documentation ensures sustainability and provides the foundation for collaborative improvement. By offering a fair and multi-faceted comparison, CODES helps researchers select the most suitable surrogate for their specific dataset and application while deepening our understanding of surrogate learning behaviour. |
| title | CODES: Benchmarking Coupled ODE Surrogates |
| topic | Machine Learning Instrumentation and Methods for Astrophysics Computational Physics |
| url | https://arxiv.org/abs/2410.20886 |