[2407.21231] Towards an Integrated Performance Framework for Fire Science and Management Workflows
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Computer Science > Machine Learning

arXiv:2407.21231 (cs)
[Submitted on 30 Jul 2024]

Title:Towards an Integrated Performance Framework for Fire Science and Management Workflows

Authors: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.
Subjects: Machine Learning (cs.LG); Performance (cs.PF)
Cite as: arXiv:2407.21231 [cs.LG]
  (or arXiv:2407.21231v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2407.21231

Submission history

From: Hena Ahmed [view email]
[v1] Tue, 30 Jul 2024 22:37:25 UTC (929 KB)
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