SORT shifts equation discovery from brittle library selection to basis design: by learning sparse coefficients in well-chosen orthogonal bases, it provides a more stable intermediate representation that gracefully degrades under noise and sampling sparsity.
SORT is a machine learning technique that learns compact mathematical representations of dynamical systems from noisy, irregularly sampled data by fitting sparse coefficients in orthogonal basis expansions.