Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations

Abstract: This work develops Laplace neural operators for multi-fidelity prediction and uncertainty quantification in parametric partial differential equations. The method combines operator learning with uncertainty-aware modeling to improve prediction efficiency across fidelities.

[arxiv]