Neural Operators for Hyperbolic PDE Backstepping Kernels
Author:
Affiliation:
1. University of California,San Diego,USA
Funder
NSF
AFOSR
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10383192/10383193/10383639.pdf?arnumber=10383639
Reference16 articles.
1. Adaptive output-feedback stabilization of non-local hyperbolic PDEs
2. Approximation rates of DeepONets for learning operators arising from advection–diffusion equations
3. Learning operators with coupled attention;Kissas;Journal of Machine Learning Research,2022
4. On universal approximation and error bounds for fourier neural operators;Kovachki;The Journal of Machine Learning Research,2021
5. Backstepping boundary control for first-order hyperbolic PDEs and application to systems with actuator and sensor delays
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1. Neural Operators for Hyperbolic PDE Backstepping Feedback Laws;2023 62nd IEEE Conference on Decision and Control (CDC);2023-12-13
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