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洞見 - Operator learning
Continuous Representation Learning for Spatiotemporal Super-Resolution of Parametric PDE Solutions
The proposed Super Resolution Operator Network (SROpNet) framework learns continuous representations of solutions to parametric differential equations from low-resolution numerical approximations, enabling spatiotemporal super-resolution without constraints on sensor and prediction locations.
Multi-Resolution Active Learning for Efficient Training of Fourier Neural Operators
The core message of this work is to propose a novel multi-resolution active learning method, called MRA-FNO, that can dynamically select the most valuable input functions and resolutions to train Fourier neural operators (FNOs) efficiently while significantly reducing the data collection cost.