3 papers
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…
cs.LG2025
Multi-Operator Few-Shot Learning for Generalization Across PDE Families
Yile Li, Shandian Zhe
Learning solution operators for partial differential equations (PDEs) has become a foundational task in scientific machine learning. However, existing neural operator methods requi…
cs.LG2025
Graph-Based Operator Learning from Limited Data on Irregular Domains
Yile Li, Shandian Zhe
Operator learning seeks to approximate mappings from input functions to output solutions, particularly in the context of partial differential equations (PDEs). While recent advance…