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Learnable Hybrid Autoencoder for Model Reduction by eroica10001 is a document available to read on EtoBox.
What is Learnable Hybrid Autoencoder for Model Reduction about?
The document presents a learnable weighted hybrid autoencoder (AE) for model order reduction, combining singular value decomposition with deep learning to improve convergence and robustness in high-dimensional physical systems. The proposed method addresses the Kolmogorov barrier and enhances generalization performance through a weighted framework, which is validated on chaotic PDE systems. Additionally, it integrates with time series modeling techniques to improve surrogate modeling for complex multiscale
- Author
- eroica10001
- Language
- EN