This paper reviews multi-fidelity surrogate modeling techniques for predicting the complex properties of composite materials. It covers methods ranging from Gaussian-process-based approaches like co-Kriging to multi-fidelity neural networks. The review examines how these techniques combine less expensive data with limited high-accuracy data to achieve reliable predictions, and discusses their applications in engineering problems such as design exploration and optimization. AI
影响 Provides a structured overview of multi-fidelity modeling techniques relevant for complex material simulations.
排序理由 This is a review paper published on arXiv discussing modeling techniques.
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