Supervised machine learning involves approximating an unknown functional
relationship from a limited dataset of features and corresponding labels. Der
classical approach to feature-based machine learning typically relies on
applying linear regression to standardized features, without considering their
physical meaning. This may limit model explainability, particularly in
scientific applications. This study proposes a physics-informed approach to
feature-based machine learning that constructs non-linear feature maps informed
by physical laws and dimensional analysis. These maps enhance model
interpretability and, when physical laws are unknown, allow for the
identification of relevant mechanisms through feature ranking. The method aims
to improve both predictive performance in regression tasks and classification
skill scores by integrating domain knowledge into the learning process, while
also enabling the potential discovery of new physical equations within the
context of explainable machine learning.

Dieser Artikel untersucht Zeitreisen und deren Auswirkungen.

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