This paper presents a mathematics-informed approach to neural operator design, building upon the theoretical framework established in our prior work. By integrating rigorous mathematical analysis with practical design strategies, we aim to enhance the stability, convergence, generalization, and computational efficiency of neural operators. We revisit key theoretical insights, including stability in high dimensions, exponential convergence, and universality of neural operators. Based on these insights, we provide detailed design recommendations, each supported by mathematical proofs and citations. Our contributions offer a systematic methodology for developing next-gen neural operators with improved performance and reliability.
The authors bind no conflicting interests.
Google Research
We thank the Google Research Division of Goggle Inc., for providing resources and mentorships so the student intern Vu-Anh may conduct this project.
Primary Language | English |
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Subjects | Mathematical Methods and Special Functions, Approximation Theory and Asymptotic Methods |
Journal Section | Articles |
Authors | |
Early Pub Date | December 23, 2024 |
Publication Date | |
Submission Date | November 4, 2024 |
Acceptance Date | November 12, 2024 |
Published in Issue | Year 2024 Volume: 6 Issue: 2 |