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arXiv · 2407.21231

Towards an Integrated Performance Framework for Fire Science and Management Workflows

H. Ahmed·R. Shende·I. Perez·D. Crawl·S. Purawat·I. Altintas
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Abstract

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.

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H. Ahmed, R. Shende, I. Perez, D. Crawl, S. Purawat, I. Altintas. 2024-07-30. Towards an Integrated Performance Framework for Fire Science and Management Workflows. https://arxiv.org/abs/2407.21231

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