Source Seeking With Nonholonomic Unicycle Without Position Measurement - Part I: Tuning of Forward Velocity. - Chunlei Zhang; Daniel B. Arnold; Nima Ghods; Antranik Antonio Siranosian; Miroslav Krstic (CDC, 2006)

Source seeking with non-holonomic unicycle without position measurement and with tuning of forward velocity. - Chunlei Zhang; Daniel Arnold 001; Nima Ghods; Antranik Antonio Siranosian; Miroslav Krstic (Syst. Control. Lett., 2007)

An architecture for enabling distributed plug load control for commercial building demand response. - Daniel Arnold 001; Michael D. Sankur; David M. Auslander (ISGT, 2013)

Real-Time Distribution Grid State Estimation with Limited Sensors and Load Forecasting. - Roel Dobbe; Daniel Arnold 001; Stephan Liu; Duncan S. Callaway; Claire J. Tomlin (ICCPS, 2016)

Synchrophasor data analytics in distribution grids. - Daniel B. Arnold; Ciaran M. Roberts; Omid Ardakanian; Emma M. Stewart (ISGT, 2017)

Forecasting-Based State Estimation for Three-Phase Distribution Systems with Limited Sensing. - Roel Dobbe; Werner van Westering; Stephan Liu; Daniel Arnold 001; Duncan S. Callaway; Claire J. Tomlin (CoRR, 2018)

Data-Driven Decentralized Optimal Power Flow. - Roel Dobbe; Oscar Sondermeijer; David Fridovich-Keil; Daniel Arnold 001; Duncan S. Callaway; Claire J. Tomlin (CoRR, 2018)

Extremum Seeking Control of Distributed Energy Resources with Decaying Dither and Equilibrium-based Switching. - Michael D. Sankur; Daniel Arnold 001 (HICSS, 2019)

Regression-based Inverter Control for Decentralized Optimal Power Flow and Voltage Regulation. - Oscar Sondermeijer; Roel Dobbe; Daniel Arnold 001; Claire J. Tomlin; Tamás Keviczky (CoRR, 2019)

Batch Measurement Extremum Seeking Control of Distributed Energy Resources to Account for Communication Delays and Information Loss. - Michael D. Sankur; Maxime Baudette; Jason S. MacDonald; Daniel B. Arnold (HICSS, 2020)

HIL-validation of an Extremum Seeking-based Controller for Advanced DER Management. - Maxime Baudette; Daniel B. Arnold; Craig Breaden; Michael D. Sankur; Duncan S. Callaway; Jason S. MacDonald (ISGT, 2020)

SoDa: An Irradiance-Based Synthetic Solar Data Generation Tool. - Ignacio Losada Carreno; Raksha Ramakrishna; Anna Scaglione; Daniel B. Arnold; Ciaran M. Roberts; Sy-Toan Ngo; Sean Peisert; David Pinney (SmartGridComm, 2020)

Deep Reinforcement Learning for DER Cyber-Attack Mitigation. - Ciaran M. Roberts; Sy-Toan Ngo; Alexandre Milesi; Sean Peisert; Daniel B. Arnold; Shammya Saha; Anna Scaglione; Nathan G. Johnson; Anton Kocheturov; Dmitriy Fradkin (SmartGridComm, 2020)

Lyapunov Stability of Smart Inverters Using Linearized DistFlow Approximation. - Shammya Shananda Saha; Daniel Arnold 001; Anna Scaglione; Eran Schweitzer; Ciaran M. Roberts; Sean Peisert; Nathan G. Johnson (CoRR, 2020)

Toward Distributed Energy Services: Decentralizing Optimal Power Flow With Machine Learning. - Roel Dobbe; Oscar Sondermeijer; David Fridovich-Keil; Daniel Arnold 001; Duncan S. Callaway; Claire J. Tomlin (IEEE Trans. Smart Grid, 2020)

Learning Behavior of Distribution System Discrete Control Devices for Cyber-Physical Security. - Ciaran M. Roberts; Anna Scaglione; Mahdi Jamei; Reinhard Gentz; Sean Peisert; Emma M. Stewart; Chuck McParland; Alex McEachern; Daniel B. Arnold (IEEE Trans. Smart Grid, 2020)

Model-Free Optimal Voltage Phasor Regulation in Unbalanced Distribution Systems. - Michael D. Sankur; Roel Dobbe; Alexandra von Meier; Daniel B. Arnold (IEEE Trans. Smart Grid, 2020)

Deep Reinforcement Learning for Mitigating Cyber-Physical DER Voltage Unbalance Attacks. - Ciaran M. Roberts; Sy-Toan Ngo; Alexandre Milesi; Anna Scaglione; Sean Peisert; Daniel B. Arnold (ACC, 2021)

Extremum Seeking Over a Discrete Action Space. - Michael D. Sankur; Daniel B. Arnold (ACC, 2021)

Understanding the Safety Requirements for Learning-based Power Systems Operations. - Yize Chen; Daniel B. Arnold; Yuanyuan Shi; Sean Peisert (CoRR, 2021)

Learning from learning machines: a new generation of AI technology to meet the needs of science. - Luca Pion-Tonachini; Kristofer E. Bouchard; Héctor García Martín; Sean Peisert; W. Bradley Holtz; Anil Aswani; Dipankar Dwivedi; Haruko M. Wainwright; Ghanshyam Pilania; Benjamin Nachman; Babetta L. Marrone; Nicola Falco; et al. (CoRR, 2021)

SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation. - Yize Chen; Yuanyuan Shi; Daniel B. Arnold; Sean Peisert (CoRR, 2021)

Reinforcement Learning using Physics Inspired Graph Convolutional Neural Networks. - Tong Wu 002; Anna Scaglione; Daniel B. Arnold (Allerton, 2022)

Adam-based Augmented Random Search for Control Policies for Distributed Energy Resource Cyber Attack Mitigation. - Daniel Arnold 001; Sy-Toan Ngo; Ciaran M. Roberts; Yize Chen; Anna Scaglione; Sean Peisert (ACC, 2022)

Adaptive Control of Distributed Energy Resources for Distribution Grid Voltage Stability. - Daniel B. Arnold; Shammya Saha; Sy-Toan Ngo; Ciaran M. Roberts; Anna Scaglione; Nathan G. Johnson; Sean Peisert; David Pinney (CoRR, 2022)

Deep Reinforcement Learning with Graph ConvNets for Distribution Network Voltage Control. - Tong Wu 002; Ignacio Losada Carreno; Anna Scaglione; Daniel B. Arnold (CoRR, 2022)

Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI. - Tong Wu 002; Anna Scaglione; Daniel Arnold 001 (CoRR, 2022)

Outer-loop Adaptive Control of Converter-Interfaced Generation for Cyber-Physical Security. - Ciaran M. Roberts; Duncan S. Callaway; Daniel Arnold 001 (HICSS, 2023)

Network-Constrained Reinforcement Learning for Optimal EV Charging Control. - Tong Wu 002; Anna Scaglione; Adrian-Petru Surani; Daniel B. Arnold; Sean Peisert (SmartGridComm, 2023)

Constrained Reinforcement Learning for Stochastic Dynamic Optimal Power Flow Control. - Tong Wu 002; Anna Scaglione; Daniel B. Arnold (CoRR, 2023)

Augmented Digital Twin for Identification of Most Critical Cyberattacks in Industrial Systems. - Bruno Paes Leao; Jagannadh Vempati; Siddharth Bhela; Tobias Ahlgrim; Daniel Arnold 001 (CoRR, 2023)

Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms. - Tong Wu 002; Ignacio Losada Carreno; Anna Scaglione; Daniel B. Arnold (IEEE Trans. Smart Grid, 2023)

A conditional GAN-based approach to build 3D facies models sequentially upwards. - Chao Sun; Vasiliy V. Demyanov; Daniel B. Arnold (Comput. Geosci., 2023)

Transferable Learning of GCN Sampling Graph Data Clusters from Different Power Systems. - Tong Wu 002; Anna Scaglione; Daniel B. Arnold; Tianyi Chen (Allerton, 2024)

Complex-Value Spatiotemporal Graph Convolutional Neural Networks and Its Applications to Electric Power Systems AI. - Tong Wu 002; Anna Scaglione; Daniel B. Arnold (IEEE Trans. Smart Grid, 2024)

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions. - Gleb Shishaev; Vasiliy V. Demyanov; Daniel B. Arnold (CoRR, 2025)