Jacobi sets track where the gradients of two scalar fields align and, in time-varying data, trace the evolution of critical points. Because these sets are often complex and visually cluttered, this work introduces a robustness-based method for computing reduced Jacobi sets that retain structurally stable features. Mathematical analysis and experiments on synthetic and real-world datasets demonstrate clearer, more effective feature tracking.

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