Sparse Identification

Sparse identification focuses on discovering the underlying governing equations of complex dynamical systems directly from observational data, prioritizing parsimonious models that balance accuracy and interpretability. Current research emphasizes robust algorithms, such as variations of Sparse Identification of Nonlinear Dynamics (SINDy), often incorporating Bayesian methods, ensembling techniques, and mixed-integer optimization to handle noisy, incomplete, or high-dimensional data. These advancements are improving the accuracy and reliability of data-driven model discovery across diverse fields, from climate science and neuroscience to engineering and medicine, enabling better understanding and prediction of complex phenomena.