Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks
Composite materials exhibit strongly hierarchical and anisotropic properties governed by coupled mechanisms spanning constituents, plies, laminates, structures, and manufacturing history. This intrinsic complexity makes predictive modeling of composites expensive, because repeated experiments and high-fidelity simulations are needed to cover large design spaces of material, structure, and manufacturing. Multi-fidelity surrogate modeling addresses this challenge by combining abundant, less expensive data with limited high-accuracy data to recover reliable high-fidelity predictions. This review presents a structured overview of multi-fidelity modeling for composite mechanics, covering Gauss...
| Authors | Haizhou Wen, Elham Kiyani, Gang Li et al. |
|---|---|
| Primary category | physics.comp-ph |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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