The parametric complexity of operator learning

View Full Paper

Ask AI

Bookmark

Authors

Discussion

This analysis demonstrates the curse of parametric complexity in operator learning, suggesting improvements via HJ-Net.

Operator learning faces a significant challenge due to parametric complexity, impacting model evaluations and emulations.
The curse of parametric complexity, a concept extending beyond traditional limits, applies across diverse neural operator architectures.
The introduction of HJ-Net addresses the Hamilton-Jacobi equation, enabling a breakthrough in overcoming parametric complexity.
Developed estimates for error and complexity of HJ-Net underscore its potential in infinite-dimensional function spaces.

Lanthaler et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark