9 citations · 9 across the 1 of their papers we have counts for
2 papers
cs.LG2025
RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains
Sepehr Mousavi, Shizheng Wen, Levi Lingsch +3
Learning the solution operators of PDEs on arbitrary domains is challenging due to the diversity of possible domain shapes, in addition to the often intricate underlying physics. W…
cs.LG2024★ 9 cited
Poseidon: Efficient Foundation Models for PDEs
Maximilian Herde, Bogdan Raonić, Tobias Rohner +4
We introduce Poseidon, a foundation model for learning the solution operators of PDEs. It is based on a multiscale operator transformer, with time-conditioned layer norms that enab…