Rahul Nellikkath

According to our database, Rahul Nellikkath authored at least 10 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Scalable Exact Verification of Optimization Proxies for Large-Scale Optimal Power Flow.

Rahul Nellikkath, Mathieu Tanneau, Pascal Van Hentenryck, Spyros Chatzivasileiadis

CoRR, 2024

Correctness Verification of Neural Networks Approximating Differential Equations.

Petros Ellinas, Rahul Nellikkath, Ignasi Ventura, Jochen Stiasny, Spyros Chatzivasileiadis

CoRR, 2024

2023

Enriching Neural Network Training Dataset to Improve Worst-Case Performance Guarantees.

Rahul Nellikkath, Spyros Chatzivasileiadis

CoRR, 2023

Physics Informed Neural Networks for Phase Locked Loop Transient Stability Assessment.

Rahul Nellikkath, Andreas Venzke, Mohammad Kazem Bakhshizadeh, Ilgiz Murzakhanov, Spyros Chatzivasileiadis

CoRR, 2023

2022

Minimizing Worst-Case Violations of Neural Networks.

Rahul Nellikkath, Spyros Chatzivasileiadis

CoRR, 2022

Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations.

Robert I. Hamilton, Jochen Stiasny, Tabia Ahmad, Samuel Chevalier, Rahul Nellikkath, Ilgiz Murzakhanov, Spyros Chatzivasileiadis, Panagiotis N. Papadopoulos

CoRR, 2022

Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems.

Jochen Stiasny, Samuel Chevalier, Rahul Nellikkath, Brynjar Sævarsson, Spyros Chatzivasileiadis

CoRR, 2022

2021

Network-Aware Flexibility Requests for Distribution-Level Flexibility Markets.

Eléa Prat, Irena Dukovska, Rahul Nellikkath, Lars Herre, Malte Thoma, Spyros Chatzivasileiadis

CoRR, 2021

Physics-Informed Neural Networks for AC Optimal Power Flow.

Rahul Nellikkath, Spyros Chatzivasileiadis

CoRR, 2021

Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow.

Rahul Nellikkath, Spyros Chatzivasileiadis

Proceedings of the IEEE International Conference on Communications, 2021