3 papers
cs.LG2026
Arbitrarily Conditioned Hierarchical Flows for Spatiotemporal Events
Keyan Chen, Qiwei Yuan, Zhitong Xu +2
Events in spatiotemporal systems are ubiquitous, yet modeling their complex distributions remains challenging. Existing point process models often rely on strong structural assumpt…
cs.LG2026
Deep Gaussian Processes for Functional Maps
Matthew Lowery, Zhitong Xu, Da Long +5
Learning mappings between functional spaces, also known as function-on-function regression, is a fundamental problem in functional data analysis with broad applications, including…
cs.LG2026
Pseudo-Physics-Informed Neural Operators: Enhancing Operator Learning from Limited Data
Keyan Chen, Yile Li, Da Long +4
Neural operators have shown great potential in surrogate modeling. However, training a well-performing neural operator typically requires a substantial amount of data, which can po…