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Figure 1. Taxonomy of multi-fidelity learning methodologies in materials design. Five principal strategies are highlighted: (i) Statistical and parametric models; (ii) Direct machine learning with fidelity features; (iii) Co-Kriging and correction-based methods; (iv) Multi-fidelity deep learning-based frameworks; and (v) Multi-fidelity active learning and adaptive sampling. These categories provide the structural roadmap for section “Strategies and applications for multi-fidelity learning in materials design”, where each approach is introduced, illustrated with representative case studies, and evaluated in terms of strengths, limitations, and domains of applicability.