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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

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Preprint Aug 2026

When Your State Estimator Has Lost The Plot: Detecting Estimator Failures Via Spectral Analysis

Reliable onboard state estimation is essential for safe robotic operation, yet unmodeled disturbances, such as sensor aliasing or out-of-distribution noise, still cause estimators to degrade or fail completely. While many methods aim to improve estimator robustness, only a few provide introspective mechanisms to assess...

Christian Lanegger, Helen Oleynikova, R. Siegwart et al. · 0 citations
Open access Aug 2026

Joint parameter estimation and abrupt change quantification with uncertainty quantification in high degree of freedom dynamical systems via an optimized adaptive unscented Kalman filter

The results demonstrate the potential of the proposed framework for addressing challenging identification problems in dynamical systems such as tracking sudden changes and evaluating the uncertainties linked to both modeling and measurement, particularly those with limited and noisy sensor data.

Esmaeil Ghorbani, Quentin Dollon, F. Gosselin · 0 citations
Preprint Sep 2026

Deformable Object Manipulation under Partial Observability via Real-Time Full-Shape Estimation

Manipulating deformable objects (DOs) is challenging due to their high-dimensional state space, underactuated dynamics, and partial observability. In this paper, we propose cRVAE, a lightweight conditional recurrent variational autoencoder that estimates the full DO state from only partial corner-node observations duri...

Kosar Behnia, Ville Kyrki, Gokhan Alcan · 1 citation
Preprint Sep 2026

Recoverability Is a Subspace Property: A Benchmark for Certified State Estimation from Partial PDE Observations

UniPDE-Bench is introduced, a direction-wise evaluation protocol that incorporates local observation geometry into state-estimation assessment, providing a reference for prediction recovery and confidence-based selection that is independent of the estimator.

Qing-Wei Dong, P. Zeng, Guang-Xi Wan et al. · 0 citations
Open access Sep 2026

Physics-Informed Digital Twin for Robust Inverse Parameter Identification under Sparse and Noisy Measurements

This work provides a comprehensive reliability-oriented evaluation by integrating optimization robustness, structural identifiability, uncertainty quantification, comparative benchmarking, and multi-noise validation within a single physics-informed digital twin framework, improving the reliability and interpretability...

Aswin Karkadakattil · 0 citations
Open access Sep 2026

Hybrid DNN and EnKF framework for dynamic state estimation in power systems

A deep neural network-based dynamic SE framework with physics-constrained and ensemble Kalman filter-guided training that jointly incorporates prior-state supervision, EnKF-based covariance-aware correction, and nonlinear AC measurement consistency is proposed.

Teng-Fei Zhang, Xue Li, Jun-Biao Yang · 0 citations

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