Deep Learning-Based Discovery of Governing Equations for Complex Climate Systems and Extreme Weather Prediction
Keywords:
deep learning, governing equation discovery, climate modeling, extreme weather prediction, socio-technical infrastructureAbstract
The accelerating frequency and intensity of extreme weather events demand a radical rethinking of how governing physical equations are derived, represented, and operationalized within Earth system models. This paper presents a system-level analysis of deep learning architectures for the automated discovery of governing equations from climate data, focusing on their integration into large-scale prediction pipelines, infrastructure requirements, robustness under distributional shift, fairness across heterogeneous observational networks, and governance implications. We examine how sparse regression, physics-informed neural networks, neural operators, and invariant-based symbolic discovery methods can bridge the gap between purely data-driven forecasting and the foundational need for interpretable, conservation-compliant dynamical representations. The discussion moves beyond algorithmic novelty to consider the structural trade-offs that emerge when deployed alongside operational numerical weather prediction and climate services. Particular attention is given to the tension between model complexity and computational sustainability, the propagation of observational bias into discovered equations, and the policy consequences of using black-box or semi-interpretable surrogates for extreme event attribution. We argue that the discovery of governing equations must be understood as a socio-technical endeavor, where choices about training data provenance, hardware footprint, uncertainty quantification, and transparency frameworks collectively determine the utility and legitimacy of resulting predictions. The paper proposes a set of system design principles that aim to align deep learning-based equation discovery with the requirements of equitable, robust, and operationally sustainable climate intelligence.
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