AI RESEARCH
Verifiable Error Bounds for Physics-Informed Neural KKL Observers
arXiv CS.LG
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ArXi:2603.20434v1 Announce Type: cross 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.