AI governance framework adapts thermal-hydraulic models

Researchers have developed a novel framework for continuously adapting AI surrogate models used in thermal-hydraulic forecasting. This system employs a multi-agent governance structure, where specialized agents monitor performance, diagnose errors, and propose model updates. The framework ensures auditable model evolution by using deterministic gates and background learning to maintain control over model replacements, significantly improving forecasting accuracy and reducing error rates compared to static models. AI

IMPACT Introduces a robust method for maintaining AI model accuracy in dynamic industrial environments, crucial for real-time forecasting applications.

RANK_REASON The cluster contains a research paper detailing a new AI framework. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Fourier Neural Operator
• Graph Neural Network
• Transformer
• Validation-Gated Multi-Agent Governance for Online Adaptation of Thermal-Hydraulic Surrogate Models under Operating-Regime Shift

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• safety
• product

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