English(EN) Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks

综述文章详述用于复合材料力学建模的多保真神经网络

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-04 17:45

本文综述了用于预测复合材料复杂特性的多保真代理建模技术。文章涵盖了从基于高斯过程的方法（如协同克里金）到多保真神经网络的各种方法。该综述探讨了这些技术如何结合成本较低的数据和有限的高精度数据来实现可靠的预测，并讨论了它们在工程问题中的应用，如设计探索和优化。 AI

影响为复杂材料模拟相关的多保真建模技术提供了结构化概述。

排序理由这是一篇在arXiv上发表的关于建模技术的综述文章。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.LG TIER_1 English(EN) · Haizhou Wen, Elham Kiyani, Gang Li, Srikanth Pilla, George Em Karniadakis, Zhen Li · 2026-05-05 04:00

Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks

arXiv:2605.02871v1 Announce Type: cross Abstract: 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…
arXiv cs.LG TIER_1 English(EN) · Zhen Li · 2026-05-04 17:45

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 repeate…