Full Domain Analysis in Fluid Dynamics
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913863801241600 |
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| author | Hagg, Alexander Gaier, Adam Wilde, Dominik Asteroth, Alexander Foysi, Holger Reith, Dirk |
| author_facet | Hagg, Alexander Gaier, Adam Wilde, Dominik Asteroth, Alexander Foysi, Holger Reith, Dirk |
| contents | Novel techniques in evolutionary optimization, simulation and machine learning allow for a broad analysis of domains like fluid dynamics, in which computation is expensive and flow behavior is complex. Under the term of full domain analysis we understand the ability to efficiently determine the full space of solutions in a problem domain, and analyze the behavior of those solutions in an accessible and interactive manner. The goal of full domain analysis is to deepen our understanding of domains by generating many examples of flow, their diversification, optimization and analysis. We define a formal model for full domain analysis, its current state of the art, and requirements of subcomponents. Finally, an example is given to show what we can learn by using full domain analysis. Full domain analysis, rooted in optimization and machine learning, can be a helpful tool in understanding complex systems in computational physics and beyond. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22275 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Full Domain Analysis in Fluid Dynamics Hagg, Alexander Gaier, Adam Wilde, Dominik Asteroth, Alexander Foysi, Holger Reith, Dirk Machine Learning Neural and Evolutionary Computing 68U01 I.2.1; I.2.6 Novel techniques in evolutionary optimization, simulation and machine learning allow for a broad analysis of domains like fluid dynamics, in which computation is expensive and flow behavior is complex. Under the term of full domain analysis we understand the ability to efficiently determine the full space of solutions in a problem domain, and analyze the behavior of those solutions in an accessible and interactive manner. The goal of full domain analysis is to deepen our understanding of domains by generating many examples of flow, their diversification, optimization and analysis. We define a formal model for full domain analysis, its current state of the art, and requirements of subcomponents. Finally, an example is given to show what we can learn by using full domain analysis. Full domain analysis, rooted in optimization and machine learning, can be a helpful tool in understanding complex systems in computational physics and beyond. |
| title | Full Domain Analysis in Fluid Dynamics |
| topic | Machine Learning Neural and Evolutionary Computing 68U01 I.2.1; I.2.6 |
| url | https://arxiv.org/abs/2505.22275 |