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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/2407.21231 |
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| _version_ | 1866910548180860928 |
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| author | Ahmed, H. Shende, R. Perez, I. Crawl, D. Purawat, S. Altintas, I. |
| author_facet | Ahmed, H. Shende, R. Perez, I. Crawl, D. Purawat, S. Altintas, I. |
| contents | Reliable performance metrics are necessary prerequisites to building large-scale end-to-end integrated workflows for collaborative scientific research, particularly within context of use-inspired decision making platforms with many concurrent users and when computing real-time and urgent results using large data. This work is a building block for the National Data Platform, which leverages multiple use-cases including the WIFIRE Data and Model Commons for wildfire behavior modeling and the EarthScope Consortium for collaborative geophysical research. This paper presents an artificial intelligence and machine learning (AI/ML) approach to performance assessment and optimization of scientific workflows. An associated early AI/ML framework spanning performance data collection, prediction and optimization is applied to wildfire science applications within the WIFIRE BurnPro3D (BP3D) platform for proactive fire management and mitigation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_21231 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards an Integrated Performance Framework for Fire Science and Management Workflows Ahmed, H. Shende, R. Perez, I. Crawl, D. Purawat, S. Altintas, I. Machine Learning Performance Reliable performance metrics are necessary prerequisites to building large-scale end-to-end integrated workflows for collaborative scientific research, particularly within context of use-inspired decision making platforms with many concurrent users and when computing real-time and urgent results using large data. This work is a building block for the National Data Platform, which leverages multiple use-cases including the WIFIRE Data and Model Commons for wildfire behavior modeling and the EarthScope Consortium for collaborative geophysical research. This paper presents an artificial intelligence and machine learning (AI/ML) approach to performance assessment and optimization of scientific workflows. An associated early AI/ML framework spanning performance data collection, prediction and optimization is applied to wildfire science applications within the WIFIRE BurnPro3D (BP3D) platform for proactive fire management and mitigation. |
| title | Towards an Integrated Performance Framework for Fire Science and Management Workflows |
| topic | Machine Learning Performance |
| url | https://arxiv.org/abs/2407.21231 |