Feb. 15, 2024, 5:42 a.m. | Yusuke Tanaka, Takaharu Yaguchi, Tomoharu Iwata, Naonori Ueda

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.09018v1 Announce Type: cross
Abstract: The operator learning has received significant attention in recent years, with the aim of learning a mapping between function spaces. Prior works have proposed deep neural networks (DNNs) for learning such a mapping, enabling the learning of solution operators of partial differential equations (PDEs). However, these works still struggle to learn dynamics that obeys the laws of physics. This paper proposes Energy-consistent Neural Operators (ENOs), a general framework for learning solution operators of PDEs that …

abstract aim arxiv attention cs.lg differential enabling energy function mapping networks neural networks operators prior solution spaces stat.ml theory type

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