4 papers
Factorized Neural Operators Decompose Dynamic and Persistent Responses
Hao Tang, Yuechen Duan, Jiongyu Zhu +3
Physical systems often exhibit heterogeneous mechanisms, where rapidly evolving dynamics coexist with persistent structures. Capturing such multiscale physical behavior remains cha…
Neural Operators for Biomedical Spherical Heterogeneity
Hao Tang, Hao Chen, Hao Li +1
Spherical deep learning has been widely applied to a broad range of real-world problems. Existing approaches often face challenges in balancing strong spherical geometric inductive…
Geometric Laplace Neural Operator
Hao Tang, Jiongyu Zhu, Zimeng Feng +2
Neural operators have emerged as powerful tools for learning mappings between function spaces, enabling efficient solutions to partial differential equations across varying inputs…
Artificial Intelligence for Central Dogma-Centric Multi-Omics: Challenges and Breakthroughs
Lei Xin, Caiyun Huang, Hao Li +8
With the rapid development of high-throughput sequencing platforms, an increasing number of omics technologies, such as genomics, metabolomics, and transcriptomics, are being appli…