5 papers
HyperKKL: Learning KKL Observers for Non-Autonomous Nonlinear Systems via Hypernetwork-Based Input Conditioning
Yahia Salaheldin Shaaban, Abdelrahman Sayed Sayed, M. Umar B. Niazi +1
Kazantzis-Kravaris/Luenberger (KKL) observers are a class of state observers for nonlinear systems that rely on an injective map to transform the nonlinear dynamics into a stable q…
HyperKKL: Enabling Non-Autonomous State Estimation through Dynamic Weight Conditioning
Yahia Salaheldin Shaaban, Salem Lahlou, Abdelrahman Sayed Sayed
This paper proposes HyperKKL, a novel learning approach for designing Kazantzis-Kravaris/Luenberger (KKL) observers for non-autonomous nonlinear systems. While KKL observers offer…
Risk Assessment of an Autonomous Underwater Snake Robot in Confined Operations
Abdelrahman Sayed Sayed
The growing interest in ocean discovery imposes a need for inspection and intervention in confined and demanding environments. Eely's slender shape, in addition to its ability to c…
Mixed Monotonicity Reachability Analysis of Neural ODE: A Trade-Off Between Tightness and Efficiency
Abdelrahman Sayed Sayed, Pierre-Jean Meyer, Mohamed Ghazel
Neural ordinary differential equations (neural ODE) are powerful continuous-time machine learning models for depicting the behavior of complex dynamical systems, but their verifica…
Bridging Neural ODE and ResNet: A Formal Error Bound for Safety Verification
Abdelrahman Sayed Sayed, Pierre-Jean Meyer, Mohamed Ghazel
A neural ordinary differential equation (neural ODE) is a machine learning model that is commonly described as a continuous-depth generalization of a residual network (ResNet) with…