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On Generalization, Language, Interpretability and the Future of Geo-Scientific Machine Learning
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DOI：10.1016/j.envsoft.2025.106834.png)
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Abstract
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There are several types of generalization ability that we may wish our models to be capable of. All but the most basic of these require the representation to be suitably interpretable so that it can provide meaningful support for scenario analysis, scientific reasoning, and decision making under system non-stationarity and model transfer. However interpretability of a model can only be meaningfully understood in the context of the ‘language’ used for its construction. In this regard it is important to recognize that, while machine-learning-based (MLB) models tend to prioritize accuracy and precision (in service of predictive performance) and physics-based (PB) models tend to emphasize physical/geo-scientific interpretability (in service of understanding), their learned representations are actually based in related but somewhat different languages, levels of linguistic abstraction, and grammatical rules.
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