Verifiable Error Bounds for Physics-Informed Neural KKL Observers
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908903321632768 |
|---|---|
| author | Berin-Costain, Hannah Wang, Harry Morris, Kirsten Liu, Jun |
| author_facet | Berin-Costain, Hannah Wang, Harry Morris, Kirsten Liu, Jun |
| contents | This paper proposes a computable state-estimation error bound for learning-based Kazantzis--Kravaris/Luenberger (KKL) observers. Recent work learns the KKL transformation map with a physics-informed neural network (PINN) and a corresponding left-inverse map with a conventional neural network. However, no computable state-estimation error bounds are currently available for this approach. We derive a state-estimation error bound that depends only on quantities that can be certified over a prescribed region using neural network verification. We further extend the result to bounded additive measurement noise and demonstrate the guarantees on nonlinear benchmark systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20434 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Verifiable Error Bounds for Physics-Informed Neural KKL Observers Berin-Costain, Hannah Wang, Harry Morris, Kirsten Liu, Jun Systems and Control Machine Learning This paper proposes a computable state-estimation error bound for learning-based Kazantzis--Kravaris/Luenberger (KKL) observers. Recent work learns the KKL transformation map with a physics-informed neural network (PINN) and a corresponding left-inverse map with a conventional neural network. However, no computable state-estimation error bounds are currently available for this approach. We derive a state-estimation error bound that depends only on quantities that can be certified over a prescribed region using neural network verification. We further extend the result to bounded additive measurement noise and demonstrate the guarantees on nonlinear benchmark systems. |
| title | Verifiable Error Bounds for Physics-Informed Neural KKL Observers |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2603.20434 |