Physics-Informed Neural Embeddings of PDE Solution Families
We introduce a physics-informed framework for learning finite-dimensional embeddings of solution families of partial differential equations. The method uses a multihead Physics-Informed Neural Network in which a shared body learns a latent manifold representing the solution space, while linear heads reconstruct individual solutions associated with different initial conditions. A head-orthogonalization penalty removes degeneracies in the latent representation and stabilizes the principal-component spectrum across training realizations. Because the initial condition is built into the network out
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- Linked via arxiv authorRaul Jimenez →
Physics-Informed Neural Embeddings of PDE Solution Families
- Linked via arxiv authorSvitlana Mayboroda →
Physics-Informed Neural Embeddings of PDE Solution Families
- Linked via arxiv authorPavlos Protopapas →
Physics-Informed Neural Embeddings of PDE Solution Families
- Linked via arxiv authorLeonid Sarieddine →
Physics-Informed Neural Embeddings of PDE Solution Families
- Linked via arxiv authorDavid N. Spergel →
Physics-Informed Neural Embeddings of PDE Solution Families
- Linked via arxiv authorPedro Tarancón-Álvarez →
Physics-Informed Neural Embeddings of PDE Solution Families