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AI-Based Model Order Reduction Techniques: A Survey
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AI
DOI：10.1007/s11831-024-10207-2.png)
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摘要
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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.
Keyword:
SYSTEM STABILITY IMPROVEMENT
PRINCIPAL COMPONENT ANALYSIS
LEARNING ALGORITHM
OPTIMAL LOCATION
NEURAL-NETWORKS
DESIGN
APPROXIMATION
PROJECTION
TURBULENCE
DYNAMICS
期刊
IF:
12.1
论文数:
1.8K
被引数:
1.2W
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