Physics-Informed Neural Embeddings of PDE Solution Families
A physics-informed framework for learning finite-dimensional embeddings of solution families of partial differential equations using a multihead Physics-Informed Neural Network is introduced, which establishes the learned spectral profiles and principal components as robust observables of solution-manifold geometry.