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20232026
most citedA hands-on introduction to Physics-Informed Neural Networks for solving partial differential equations with benchmark tests taken from astrophysics and plasma physics

2 citations · 2 across the 4 of their papers we have counts for

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astro-ph.SR2025

Physics-Informed Neural Networks for Solving Forward and Inverse PDEs with Limited and Noisy Data: Application to Solar Corona Modeling

Hubert Baty

I will demonstrate the effectiveness of Physics-Informed Neural Networks (PINNs) in solving partial differential equations (PDEs) when training data are scarce or noisy. The traini…

astro-ph.SR2023

Modelling solar coronal magnetic fields with physics-informed neural networks

Hubert Baty, Vincent Vigon

We present a novel numerical approach aiming at computing equilibria and dynamics structures of magnetized plasmas in coronal environments. A technique based on the use of neural n…