Samuel Chevalier

Orcid: 0000-0003-2426-1857

According to our database1, Samuel Chevalier authored at least 29 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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On csauthors.net:

Bibliography

2024
GPU-Accelerated Verification of Machine Learning Models for Power Systems.
Proceedings of the 57th Hawaii International Conference on System Sciences, 2024

2023
Towards Energysheds: A Technical Definition and Cooperative Planning Framework.
CoRR, 2023

Towards Perturbation-Induced Static Pivoting on GPU-Based Linear Solvers.
CoRR, 2023

A Parallelized, Adam-Based Solver for Reserve and Security Constrained AC Unit Commitment.
CoRR, 2023

Scalable Bilevel Optimization for Generating Maximally Representative OPF Datasets.
Proceedings of the IEEE PES Innovative Smart Grid Technologies Europe, 2023

Resilient Feature-driven Trading of Renewable Energy with Missing Data.
Proceedings of the IEEE PES Innovative Smart Grid Technologies Europe, 2023

Emission-Constrained Optimization of Gas Networks: Input-Convex Neural Network Approach.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

2022
Global Performance Guarantees for Neural Network Models of AC Power Flow.
CoRR, 2022

Emission-Aware Optimization of Gas Networks: Input-Convex Neural Network Approach.
CoRR, 2022

Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations.
CoRR, 2022

Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems.
CoRR, 2022

Optimization-Based Exploration of the Feasible Power Flow Space for Rapid Data Collection.
Proceedings of the IEEE International Conference on Communications, 2022

Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers.
Proceedings of the Learning for Dynamics and Control Conference, 2022

Towards Optimal Kron-based Reduction Of Networks (Opti-KRON) for the Electric Power Grid.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

2021
Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitment.
CoRR, 2021

Uncertainty Quantification in LV State Estimation Under High Shares of Flexible Resources.
CoRR, 2021

Contracting Neural-Newton Solver.
CoRR, 2021

Learning without Data: Physics-Informed Neural Networks for Fast Time-Domain Simulation.
Proceedings of the IEEE International Conference on Communications, 2021

2020
Handling Initial Conditions in Vector Fitting for Real Time Modeling of Power System Dynamics.
CoRR, 2020

Stability Certification Standards for DC Microgrid Networks with Arbitrary Load Configurations.
CoRR, 2020

Accelerated Probabilistic State Estimation in Distribution Grids via Model Order Reduction.
CoRR, 2020

Accelerated Probabilistic Power Flow via Model Order Reduction and Neumann Series Expansion.
CoRR, 2020

Dynamic Linepack Depletion Models for Natural Gas Pipeline Networks.
CoRR, 2020

2019
A Passivity Enforcement Technique for Forced Oscillation Source Location.
CoRR, 2019

Decentralized stability rules for microgrids.
Proceedings of the 2019 American Control Conference, 2019

2018
Using Passivity Theory to Interpret the Dissipating Energy Flow Method.
CoRR, 2018

A Bayesian Approach to Forced Oscillation Source Location Given Uncertain Generator Parameters.
CoRR, 2018

Mitigating the Risk of Voltage Collapse using Statistical Measures from PMU Data.
CoRR, 2018

2017
Using Effective Generator Impedance for Forced Oscillation Source Location.
CoRR, 2017


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