arXiv:2606. 24851v1 Announce Type: new Abstract: Fourier Neural Operators (FNO) learn solution operators of partial differential equations by parameterizing global convolutions in the complex Fourier domain.
By Jason Sulskis, Sathya Ravi
arXiv:2606. 28122v1 Announce Type: cross Abstract: Neural operators provide deep neural networks for learning mappings between function spaces.
By Alex Colagrande, Paul Caillon, Eva Feillet, Alexandre Allauzen
arXiv:2606. 27459v2 Announce Type: replace Abstract: We consider the cubic nonlinear Schr\"odinger (NLS) equation on two-dimensional flat tori with varying aspect ratios.
By Emmanuel E. Oguadimma, Victory C. Obieke, Xueying Yu
arXiv:2606. 27459v1 Announce Type: new Abstract: We consider the cubic nonlinear Schr\"odinger (NLS) equation on two-dimensional flat tori with varying aspect ratios.
By Emmanuel E. Oguadimma, Victory C. Obieke, Xueying Yu
arXiv:2602. 12706v2 Announce Type: replace Abstract: Neural operators have emerged as fast surrogate solvers for parametric partial differential equations (PDEs).
By Heechang Kim, Qianying Cao, Hyomin Shin, Seungchul Lee, George Em Karniadakis, Minseok Choi
arXiv:2606. 11518v1 Announce Type: cross Abstract: Fourier neural operators (FNOs) are effective and efficient surrogates for approximating solutions of PDEs and generalize across discretizations.
By Pengqing Shi, Jie Yin, Stephen Tierney, Junbin Gao