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Residuals Are Not Enough: Limits of Physics-Informed Pre-Training for Scientific Foundation Models
Scientific foundation models (SciFMs) aim to learn generalizable representations of physical systems governed by partial differential equations (PDEs), enabling transfer across tasks and domains. While physics-informed methods, which leverage PDE residuals as supervisory signals, have shown promise
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