Knowledge‐Guided Machine Learning for Global Change Ecology Research

Global change ecology demands predictive models that reconcile data-driven learning with mechanistic theory to address complex, interconnected ecosystem challenges. Traditional process-based approaches struggle with spatiotemporal parameterization, while purely data-driven machine learning approaches suffer from extrapolation, interpretability, and physical consistency. Knowledge-guided machine learning (KGML) bridges this divide by systematically integrating ecological principles (e.g., physical first principles, stoichiometry, process understanding, disturbance regimes) into how models are designed, trained, and adjusted to generalize across different ecosystems. The emerging KGML paradigm offers tremendous…

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  Award: 42530109