<ul><li>Neural networks trained on large data sets lack the simplicity of traditional scientific models, leading to a growing emphasis on interpretability in scientific machine learning.</li><li>Researchers in the physical sciences not only seek predictive models but also aim to understand the fundamental principles governing systems.</li><li>Existing definitions of interpretability in equation discovery and symbolic regression tend to equate sparsity with interpretability, which is challenged by the proposed operational definition emphasizing understanding mechanisms over mathematical sparsity.</li><li>A precise and philosophically informed definition of interpretability in scientific machine learning can help overcome obstacles and advance towards a data-driven scientific future.</li></ul>

On the definition and importance of interpretability in scientific machine learning

Discover more