Researchers have developed a novel multi-agent governance framework designed to enable online adaptation of thermal-hydraulic surrogate models. This system uses distinct agents for monitoring, diagnosis, adaptation, safety auditing, and orchestration to manage model updates. The framework demonstrated a 19.0% improvement in forecasting accuracy compared to static deployment, achieving a mean absolute error of 5.72 and reducing warning exceedances to 35.8%. This approach allows for auditable surrogate evolution while maintaining control over model deployment. AI
IMPACT Introduces a novel framework for adaptive AI model deployment in critical systems, potentially improving forecasting accuracy and reliability.
RANK_REASON The cluster contains an academic paper detailing a new AI methodology.
Read on arXiv cs.MA (Multiagent) →
- 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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