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cs.LG2026
Physics-Informed Neural Embeddings of PDE Solution Families
Raul Jimenez, Svitlana Mayboroda, Pavlos Protopapas +3
We introduce a physics-informed framework for learning finite-dimensional embeddings of solution families of partial differential equations. The method uses a multihead Physics-Inf…
cs.LG2026
Recovering Sharp Conductivity Features in the Finite-Data Calderón Problem with Physics-Informed Neural Networks
Ali AlHadi Kalout, Pablo Tejerina-Pérez, Konstantin Karchev +5
Physics-informed neural networks (PINNs) have recently emerged as a promising framework for addressing the Calderón inverse problem from limited boundary data. In this work, we rev…