Cover Image

Physics-Informed Graph Neural Jump ODEs for Cascading Failure Prediction in Power Grids

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sevak, Birva, Jadhav, Shrenik, Bui, Van-Hai
Format:	Preprint
Published:	2026
Subjects:	Systems and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Enhancing Power Flow Estimation with Topology-Aware Gated Graph Neural Networks
by: Jadhav, Shrenik, et al.
Published: (2025)

Scalable Fairness Shaping with LLM-Guided Multi-Agent Reinforcement Learning for Peer-to-Peer Electricity Markets
by: Jadhav, Shrenik, et al.
Published: (2025)

FairMarket-RL: LLM-Guided Fairness Shaping for Multi-Agent Reinforcement Learning in Peer-to-Peer Markets
by: Jadhav, Shrenik, et al.
Published: (2025)

Co-Design Optimization for Data Center Cooling System via Digital Twin
by: Jadhav, Shrenik, et al.
Published: (2026)

Digital Twin-Based Cooling System Optimization for Data Center
by: Jadhav, Shrenik, et al.
Published: (2026)

Machine Learning Guided Cooling System Optimization for Data Center
by: Jadhav, Shrenik, et al.
Published: (2026)

Power Failure Cascade Prediction using Graph Neural Networks
by: Chadaga, Sathwik, et al.
Published: (2024)

Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation
by: Meng, Bo, et al.
Published: (2025)

ANN-Based Grid Impedance Estimation for Adaptive Gain Scheduling in VSG Under Dynamic Grid Conditions
by: Hoang, Quang-Manh, et al.
Published: (2025)

A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications
by: Bui, Van-Hai, et al.
Published: (2024)

Transformer-Based Model Predictive Path Integral Control
by: Zinage, Shrenik, et al.
Published: (2024)

Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systems
by: Authier, Jules, et al.
Published: (2023)

Contingency Identification of Cascading Failures in Power Transmission Networks
by: Zhai, Chao, et al.
Published: (2019)

PSO-based Sliding Mode Current Control of Grid-Forming Inverter in Rotating Frame
by: Hoang, Quang-Manh, et al.
Published: (2025)

Expressivity of Quadratic Neural ODEs
by: Hanson, Joshua, et al.
Published: (2025)

Cascading Failure Prediction via Causal Inference
by: Ghosh, Shiuli Subhra, et al.
Published: (2024)

Machine Learning Applications in Cascading Failure Analysis in Power Systems: A Review
by: Sami, Naeem Md, et al.
Published: (2023)

Optimal Parameter Design for Power Electronic Converters Using a Probabilistic Learning-Based Stochastic Surrogate Model
by: Mahajan, Akash, et al.
Published: (2025)

On Physics-Informed Neural Network Control for Power Electronics
by: Hui, Peifeng, et al.
Published: (2024)

Modular Control of Discrete Event System for Modeling and Mitigating Power System Cascading Failures
by: Al-Rousan, Wasseem, et al.
Published: (2025)

A Novel Topology Recovery Method for Low Voltage Distribution Networks
by: Mohammadi, Sina, et al.
Published: (2025)

Physics-Informed Inductive Biases for Voltage Prediction in Distribution Grids
by: Okoyomon, Ehimare, et al.
Published: (2025)

Physics-Embedded Neural ODEs for Sim2Real Edge Digital Twins of Hybrid Power Electronics Systems
by: Zheng, Jialin, et al.
Published: (2025)

Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Grids
by: Dogoulis, Pantelis, et al.
Published: (2026)

Cascading Blackout Severity Prediction with Statistically-Augmented Graph Neural Networks
by: Gorka, Joe, et al.
Published: (2024)

PowerGNN: A Topology-Aware Graph Neural Network for Electricity Grids
by: Suri, Dhruv, et al.
Published: (2025)

Physics-Embedded Neural ODEs for Learning Antagonistic Pneumatic Artificial Muscle Dynamics
by: Wang, Xinyao, et al.
Published: (2026)

Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms
by: Wu, Tong, et al.
Published: (2022)

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components
by: Karampinis, Ioannis, et al.
Published: (2025)

Modeling Power Systems Dynamics with Symbolic Physics-Informed Neural Networks
by: Tran, Huynh T. T., et al.
Published: (2023)

Physics-Informed Neural Network-Based Control for Grid-Forming Converter's Stability Under Overload Conditions
by: Kumar, Abhay, et al.
Published: (2025)

Neural Operators for Power Systems: A Physics-Informed Framework for Modeling Power System Components
by: Karampinis, Ioannis, et al.
Published: (2025)

Input-Output Specifications of Grid-Forming Functions and Data-Driven Verification Methods
by: Bui, Jennifer T., et al.
Published: (2024)

From NeurODEs to AutoencODEs: a mean-field control framework for width-varying Neural Networks
by: Cipriani, Cristina, et al.
Published: (2023)

Sparse Neural Approximations for Bilevel Adversarial Problems in Power Grids
by: Cho, Young-ho, et al.
Published: (2025)

Quantitative Flow Approximation Properties of Narrow Neural ODEs
by: Elamvazhuthi, Karthik
Published: (2025)

Restoring Feasibility in Power Grid Optimization: A Counterfactual ML Approach
by: Mohammadian, Mostafa, et al.
Published: (2025)

Physics-Informed Neural Networks in Power System Dynamics: Improving Simulation Accuracy
by: Nadal, Ignasi Ventura, et al.
Published: (2025)

Input-Output Specifications and Dynamic Droop Coefficients: Stability and Performance Conditions for Grid-Forming IBRs
by: Bui, Jennifer T., et al.
Published: (2026)

Scalable Impedance Identification of Diverse IBRs via Cluster-Specialized Neural Networks
by: Hoang, Quang Manh, et al.
Published: (2026)