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The article discusses the limitations of neural surrogates in scientific machine learning for multiscale partial differential equations (PDEs), highlighting issues such as spectral bias and irreversible information loss due to coarse-graining. It emphasizes that while neural networks can interpolate well in low-dimensional spaces, they struggle with high-frequency content crucial for accurate predictions. The paper calls for better evaluation standards and suggests hybrid approaches combining neural and cla
- Author
- Taylor
- Language
- EN