Towards an Integrated Performance Framework for Fire Science and Management Workflows
<|reference_start|>Towards an Integrated Performance Framework for Fire Science and Management Workflows: 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.<|reference_end|>
@article{ahmed2024towards,
    title={Towards an Integrated Performance Framework for Fire Science and
  Management Workflows},
    author={H. Ahmed, R. Shende, I. Perez, D. Crawl, S. Purawat, I. Altintas},
    journal={arXiv preprint arXiv:2407.21231},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21231},
    primaryClass={cs.LG cs.PF}
}