基于人工智能的模型降阶技术综述

IF 12.1 2区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Archives of Computational Methods in Engineering Pub Date : 2025-01-06 DOI:10.1007/s11831-024-10207-2

Swaroop Mallick, Monika Mittal

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引用次数: 0

摘要

模型降阶（MOR）技术在降低高维数学模型的计算复杂度，实现高效的仿真和分析方面起着至关重要的作用。近年来，人工智能（AI）已经成为包括人工智能在内的各个领域的强大工具。本调查报告概述了基于人工智能的MOR技术，探讨了人工智能方法如何集成到传统的MOR方法中。本文讨论了不同的人工智能算法，如机器学习、深度学习和进化计算，以及它们在MOR中的应用。重点介绍了基于人工智能的MOR技术的优势、挑战和未来发展方向。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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AI-Based Model Order Reduction Techniques: A Survey

Model Order Reduction (MOR) techniques play a crucial role in reducing the computational complexity of high-dimensional mathematical models, enabling efficient simulations and analysis. In recent years, Artificial Intelligence (AI) has emerged as a powerful tool in various domains, including MOR. This survey paper provides an overview of AI-based MOR techniques, exploring how AI methods are being integrated into traditional MOR approaches. Different AI algorithms, such as machine learning, deep learning, and evolutionary computing, and their applications in MOR are discussed in this paper. The advantages, challenges, and future directions of AI-based MOR techniques are also highlighted.

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来源期刊

Archives of Computational Methods in Engineering 工程技术-工程：综合

CiteScore

19.80

自引率

4.10%

发文量

153

审稿时长

>12 weeks

期刊介绍： Archives of Computational Methods in Engineering Aim and Scope: Archives of Computational Methods in Engineering serves as an active forum for disseminating research and advanced practices in computational engineering, particularly focusing on mechanics and related fields. The journal emphasizes extended state-of-the-art reviews in selected areas, a unique feature of its publication. Review Format: Reviews published in the journal offer: A survey of current literature Critical exposition of topics in their full complexity By organizing the information in this manner, readers can quickly grasp the focus, coverage, and unique features of the Archives of Computational Methods in Engineering.