scientific modelling

Explore how constrained learning keeps AI models physically accurate in science

A machine learning model can match your data perfectly and still be physically wrong, creating mass out of nothing or drifting into impossible regimes. That gap between statistical fit and physical reality is exactly what the CL4SM session at ELLIs UnConference tackles. This call for contributions invites researchers to share methods that enforce conservation laws, stability, and symmetry directly within models. If you are curious about how hierarchical routing cuts memory while preserving long-context accuracy, our related coverage explores similar themes of efficient structure.