May 7, 2024, 4:44 a.m. | Wei Xiong, Xiaomeng Huang, Ziyang Zhang, Ruixuan Deng, Pei Sun, Yang Tian

cs.LG updates on arXiv.org arxiv.org

arXiv:2301.10022v2 Announce Type: replace
Abstract: The lacking of analytic solutions of diverse partial differential equations (PDEs) gives birth to a series of computational techniques for numerical solutions. Although numerous latest advances are accomplished in developing neural operators, a kind of neural-network-based PDE solver, these solvers become less accurate and explainable while learning long-term behaviors of non-linear PDE families. In this paper, we propose the Koopman neural operator (KNO), a new neural operator, to overcome these challenges. With the same objective …

abstract advances arxiv become birth computational cs.lg cs.na differential diverse free kind latest linear math.na mesh network non-linear numerical operators physics.comp-ph physics.data-an physics.flu-dyn series solutions solver type

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