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Geometric mappings can dramatically improve PINN training, boosting accuracy and stability by aligning input coordinates with the underlying physics of PDEs.
Topological data analysis reveals that structurally stable and compact soft prompts lead to better downstream performance, enabling a new loss function (TSLoss) that improves convergence and tuning performance.
