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Towards an Integrated Performance Framework for Fire Science and Management Workflows
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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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Cited by 1 Pith paper
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BanditWare: A Contextual Bandit-based Framework for Hardware Prediction
BanditWare uses a decaying epsilon-greedy contextual bandit with linear runtime models to recommend hardware for scientific workflows, learning online with far fewer samples than offline ML approaches.
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