Learning embeddings of non-linear PDEs: the Burgers' equation

ArXi:2603.07812v1 Announce Type: cross Embeddings provide low-dimensional representations that organize complex function spaces and generalization. They provide a geometric representation that s efficient retrieval, comparison, and generalization. In this work we generalize the concept to Physics Informed Neural Networks. We present a method to construct solution embedding spaces of nonlinear partial differential equations using a multi-head setup, and extract non-degenerate information from them using principal component analysis.