Data-Driven Reduced-Complexity Modeling of Fluid Flows: A Community Challenge
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911365673779200 |
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| author | Schmidt, Oliver T. Towne, Aaron Lozano-Duran, Adrian Dawson, Scott T. M. Vinuesa, Ricardo |
| author_facet | Schmidt, Oliver T. Towne, Aaron Lozano-Duran, Adrian Dawson, Scott T. M. Vinuesa, Ricardo |
| contents | We introduce a community challenge designed to facilitate direct comparisons between data-driven methods for compression, forecasting, and sensing of complex aerospace flows. The challenge is organized into three tracks that target these complementary capabilities: compression (compact representations for large datasets), forecasting (predicting future flow states from a finite history), and sensing (inferring unmeasured flow states from limited measurements). Across these tracks, multiple challenges span diverse flow datasets and use cases, each emphasizing different model requirements. The challenge is open to anyone, and we invite broad participation to build a comprehensive and balanced picture of what works and where current methods fall short. To support fair comparisons, we provide standardized success metrics, evaluation tools, and baseline implementations, with one classical and one machine-learning baseline per challenge. Final assessments use blind tests on withheld data. We explicitly encourage negative results and careful analyses of limitations. Outcomes will be disseminated through an AIAA Journal Virtual Collection and invited presentations at AIAA conferences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06183 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data-Driven Reduced-Complexity Modeling of Fluid Flows: A Community Challenge Schmidt, Oliver T. Towne, Aaron Lozano-Duran, Adrian Dawson, Scott T. M. Vinuesa, Ricardo Machine Learning Chaotic Dynamics Computational Physics Fluid Dynamics We introduce a community challenge designed to facilitate direct comparisons between data-driven methods for compression, forecasting, and sensing of complex aerospace flows. The challenge is organized into three tracks that target these complementary capabilities: compression (compact representations for large datasets), forecasting (predicting future flow states from a finite history), and sensing (inferring unmeasured flow states from limited measurements). Across these tracks, multiple challenges span diverse flow datasets and use cases, each emphasizing different model requirements. The challenge is open to anyone, and we invite broad participation to build a comprehensive and balanced picture of what works and where current methods fall short. To support fair comparisons, we provide standardized success metrics, evaluation tools, and baseline implementations, with one classical and one machine-learning baseline per challenge. Final assessments use blind tests on withheld data. We explicitly encourage negative results and careful analyses of limitations. Outcomes will be disseminated through an AIAA Journal Virtual Collection and invited presentations at AIAA conferences. |
| title | Data-Driven Reduced-Complexity Modeling of Fluid Flows: A Community Challenge |
| topic | Machine Learning Chaotic Dynamics Computational Physics Fluid Dynamics |
| url | https://arxiv.org/abs/2601.06183 |