Author

Anne M. Tumlin

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#artificial intelligence Open access Sep 2026

Reachability-Based Formal Verification of Graph Neural Networks with Node and Edge Features

Graph neural networks (GNNs) have become a prominent approach for developing fast, topology-aware surrogates in electric power systems, supporting tasks such as power flow (PF) analysis, optimal power flow (OPF) estimation, and cascading failure analysis (CFA). Despite this growing use, formally verifying GNN-based mod...

Anne M. Tumlin, Ben Wooding, Zhenxuan Shao et al. · SAIV · 1 citation

#artificial intelligence Review Sep 2026

NNV3: Expanding Neural Network Verification to New Architectures and Domains

NNV3 is presented, the latest version of the Neural Network Verification tool, a MATLAB framework for formal verification of deep learning models and learning-enabled cyber-physical systems, demonstrating NNV's maturation into a comprehensive, robust, and accessible verification tool for a diverse range of AI systems.

Anne M. Tumlin, Samuel Sasaki, Ben Wooding et al. · 0 citations

Preprint Aug 2026

RAD: Rule-Augmented Relational Anomaly Detection

RAD is proposed, a rule-augmented relational anomaly detector that combines heterogeneous graph representation learning with refined symbolic rule signals and improves anomaly ranking over flattened tabular detectors and relational baselines under natural class imbalance.

Noah Dahle, Anne M. Tumlin, N. Tran et al. · 0 citations