no code implementations • 28 Mar 2024 • Yunwen Yin, Liang Yan
It is well known that traditional deep learning relies solely on data, which may limit its performance for the inverse problem when only indirect observation data and a physical model are available.
no code implementations • 27 Oct 2023 • Zhiwei Gao, Liang Yan, Tao Zhou
To this end, we develop an adaptive operator learning framework that can reduce modeling error gradually by forcing the surrogate to be accurate in local areas.
1 code implementation • 18 Mar 2023 • Liang Yan, Shengzhong Zhang, Bisheng Li, Min Zhou, Zengfeng Huang
To select which unlabeled nodes to add, we propose geometric ranking to rank unlabeled nodes.
no code implementations • 3 Feb 2023 • Zhiwei Gao, Tao Tang, Liang Yan, Tao Zhou
The second extension is to present the subset simulation algorithm as the posterior model (instead of the truncated Gaussian model) for estimating the error indicator, which can more effectively estimate the failure probability and generate new effective training points in the failure region.
no code implementations • 1 Oct 2022 • Zhiwei Gao, Liang Yan, Tao Zhou
For instance, a fixed set of (prior chosen) training points may fail to capture the effective solution region (especially for problems with singularities).
no code implementations • Engineering Applications of Artificial Intelligence 2021 • Kongjing Gu, Ziyang Mao, Xiaojun Duan, Guanlin Wu, Liang Yan
In summary, the proposed framework is a flexible and scalable time series clustering method that can solve various time series clustering problems especially the trajectory clustering of the UAV swarm and has great potential for general time series analysis.