arXiv stat.ML
· Papers
Interpretability and Generalization Bounds for Learning Spatial Physics
arXiv:2506.15199v4 Announce Type: replace-cross Abstract: While there are many applications of ML to scientific problems that look promising, visuals can be deceiving. Using numerical analysis techniques, we rigorously quantify the accuracy, convergence rates, and generalization bounds of certain ML models applied to l