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A novel data‐driven methodology is presented for developing mathematical models for crystallization processes. The data‐driven approach is based on the sparse identification of nonlinear dynamics (SINDy) method, which iterates between a partial least‐squares fit and a sparsity‐promoting step leading to the discovery of sparse interpretable models. The performance of the SINDy methodology is characterized...