![[D]High Accuracy (R^2 > 0.95) on Test Data but poor generalization on unseen physics data. Overfitting?](https://www.digitado.com.br/wp-content/uploads/2026/01/7Sk4uf-CDlmW2J6pY_Nhtl89dwe6kB4VlWhNYN4NUgQ-zq0tXc-978x400.jpg "[D]High Accuracy (R^2 > 0.95) on Test Data but poor generalization on unseen physics data. Overfitting?")
[D]High Accuracy (R^2 > 0.95) on Test Data but poor generalization on unseen physics data. Overfitting?
![[D]High Accuracy (R^2 > 0.95) on Test Data but poor generalization on unseen physics data. Overfitting?](https://b.thumbs.redditmedia.com/7Sk4uf-CDlmW2J6pY_Nhtl89dwe6kB4VlWhNYN4NUgQ.jpg "[D]High Accuracy (R^2 > 0.95) on Test Data but poor generalization on unseen physics data. Overfitting?") |
I’m training a Neural Network to act as a surrogate for FEA simulations The model performs amazing on the test set. See attached scatter plots . When I run a sensitivity analysis (sweeping one variable), the model outputs predictions that don’t match the physics or known trends of the motor design. It seems my model is memorizing the training cloud but not learning the underlying function.Has anyone dealt with this in Engineering/Physics datasets?Would switching to a Gaussian Process (Kriging) or adding Physics-Informed constraints (PINN) help with this specific interpolation vs. extrapolation issue? Thanks! submitted by /u/Particular_Cut_1075 |
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