incremental-svd

Introduces in-span learning, a method to adapt reduced-order models by streaming the model's own predictions through an incremental singular-value decomposition, reweighting and realigning the basis without changing the subspace. The approach is demonstrated on several dynamical systems and proposed as a computational-science analogue of in-context learning.