2 papers
cs.LG2026
Multi-stage neural operator learning with application for convolutions
Zhiping Mao, Zhenye Wen, Yong Zhang +1
Convolution integrals widely exist in applications, and to enable fast and accurate computations, this paper introduces two general multi-stage neural operator learning frameworks.…
cs.LG2025
Unsupervised operator learning approach for dissipative equations via Onsager principle
Zhipeng Chang, Zhenye Wen, Xiaofei Zhao
Existing operator learning methods rely on supervised training with high-fidelity simulation data, introducing significant computational cost. In this work, we propose the deep Ons…