Interpretability in SciML requires mechanistic understanding rather than sparsity, and prior knowledge is often essential for interpretable scientific discovery.
Automated reverse engineering of nonlinear dynamical systems.Proceedings of the National Academy of Sciences of the United States of America, 104(24):9943–9948, June 2007
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On the definition and importance of interpretability in scientific machine learning
Interpretability in SciML requires mechanistic understanding rather than sparsity, and prior knowledge is often essential for interpretable scientific discovery.