2 papers
stat.ML2026
Neural Scaling Laws of Transformer Operator Network
Haoran Yan, Zhongjie Shi, Yuanzhe Xi +2
Transformers have emerged as powerful architectures for learning solution operators of physical systems. Empirically the prediction error has been observed to decrease when the dat…
cs.LG2026
Error-Conditioned Neural Solvers
Haina Jiang, Liam Wang, Peng-Chen Chen +4
Neural surrogate models offer fast approximate mappings from PDE parameters to solutions, but they typically treat solving as a purely statistical task: once trained, they struggle…