13 citations · 18 across the 8 of their papers we have counts for
3 papers · 1 filter
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…
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…
Efficient PINNs via Multi-Head Unimodular Regularization of the Solutions Space
Pedro Tarancón-Álvarez, Pablo Tejerina-Pérez, Raul Jimenez +1
Non-linear differential equations are a fundamental tool to describe different phenomena in nature. However, we still lack a well-established method to tackle stiff differential eq…