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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…
cs.LG2025★ 4 cited
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…