Demonstrates advances in data-driven and hybrid modeling approaches for computational mechanics, implying new industrial applications.
The present paper revisits recent challenges in computational mechanics where data-driven modeling offers unexpected possibilities. For that purpose, the main concepts related to data and learning are first introduced. Then, physics-based, data-driven, and hybrid modeling approaches in the different domains of mechanics: solids and structures, fluids and flow, and processing and manufacturing will be addressed. Finally, technology needs, recent advances, and remaining challenges in the industrial sector will be highlighted.
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Chinesta et al. (2026) studied this question.
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