Transfer Learning with Convolutional Neural Operators for Solving Partial Differential Equations
Published:Dec 19, 2025 03:55
•1 min read
•ArXiv
Analysis
This research explores the application of transfer learning using convolutional neural operators to solve partial differential equations (PDEs), a critical area for scientific computing. The study's focus on transfer learning suggests potential for efficiency gains and broader applicability of PDE solvers.
Key Takeaways
Reference
“The paper uses convolutional-neural-operator-based transfer learning.”