arXiv cs.LG
· Papers
SciML in the Wild: A Diagnostic Study of When Structural Priors Help and When They Hurt
arXiv:2607.09684v1 Announce Type: new Abstract: Scientific Machine Learning (SciML) methods such as Neural Ordinary Differential Equations (NODEs), Physics-Informed Neural Networks (PINNs), and Universal Differential Equations (UDEs) are most effective when structural priors reflect reliable governing dynamics. We ask