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MsFEM-Inspired CNNs with Transfer Learning for Multiscale Model Reduction

arXiv:2606.01259v1 Announce Type: new Abstract: Deep learning-based surrogate models have been extensively developed for efficiently approximating multiscale systems with random input fields. However, most existing approaches require retraining neural networks from scratch when source terms, boundary conditions, or differential operators change, resulting in significant computational costs and limited adaptability.

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