PNNL

Draguna Vrabie serves as interim Director of the Advanced Computing, Mathematics, and Data Division in the Physical and Computational Sciences Directorate. In this role, she leads the division’s strategic planning, capability development, and operational execution. Her leadership ensures alignment with institutional goals, fosters scientific and technical innovation, and strengthens internal and external partnerships across national security, energy, and scientific mission areas. As part of the PCSD Leadership Team, Vrabie plays a key role and contributes to shaping the directorate’s technical strategy, stewarding critical capabilities, and supporting cross-laboratory initiatives that advance data-driven science, scalable computing, and intelligent decision systems.

In January 2025, Vrabie was named Deputy Director for the Advanced Computing, Mathematics, and Data Division. From 2021 to 2024, Vrabie was the Team Leader for the Autonomous Intelligence Team within the Data Sciences and Machine Intelligence group, driving advancements in AI/ML-based control, optimization, reinforcement learning, and scientific machine learning. Since 2023, she has led the Autonomous Science strategy for the Physical and Computational Sciences Directorate, aligning foundational research in AI, control, and automation with application to scientific discovery. 

Vrabie joined PNNL in 2015 as a Senior Staff Scientist in the Energy and Environment Directorate, developing PNNL's capability for adaptive and predictive control for high-performance building energy systems. Between 2015 and 2017, she led the Control Theory portfolio part of PNNL's Control of Complex Systems Initiative, which focused on learning-based control, optimization-based control, and scalable computational methods for systems and control co-design. In 2019, Vrabie was named Chief Data Scientist. Between 2019 and 2023, she served as thrust leader for PNNL’s Data Model Convergence Initiative, where she developed and stewarded a Converged Applications project portfolio focused on algorithmic and computational methods for applications that integrate scientific modeling and simulation with data analytics and machine learning. Before joining PNNL, she was a Senior Scientist at United Technologies Research Center in the Control Systems Group, contributing to various commercial and aerospace applications, including energy-efficient buildings, power electronics, jet engine fleet maintenance, and mission planning for unmanned aircraft. She holds a Ph.D. in Electrical Engineering (2009) from the University of Texas at Arlington, and an M.E. and B.E. in Automatic Control and Computer Engineering from Gheorghe Asachi Technical University in Iași, Romania, where she specialized in predictive and learning-based control and robotics.

Vrabie's research bridges control theory, artificial intelligence, and scientific machine learning, advancing adaptive decision-making for high-performance cyber-physical systems where computational intelligence (software, algorithms, AI) operates in real time alongside physical processes (mechanical, electrical, thermal, etc.) to achieve precise, efficient, and reliable performance under dynamic conditions. She has made significant contributions to the theory of reinforcement learning, optimal control, and adaptive dynamic programming, with applications in robotics, energy systems, secure control, and autonomous systems.

Vrabie specializes in model predictive control, neural modeling, and scientific machine learning, advancing methodologies for physics-informed learning, differentiable predictive control, and constrained neural models with stability guarantees. Her team develops NeuroMANCER, PNNL’s most popular open-source differentiable programming library, which enables parametric constrained optimization, physics-informed system identification, and model-based optimal control. She is the co-author of highly cited works in reinforcement learning and control. She has co-authored three monographs, including Optimal Control and Reinforcement Learning and Adaptive Dynamic Programming for Feedback Control. 

Vrabie also holds several patents across a range of application areas, including cybersecurity for critical infrastructure, AI/ML-based optimal asset management and predictive maintenance for real-time decision support for aerospace and industrial systems, power and energy system optimization, with a focus on intelligent load response, and grid stability, intelligent automation for smart buildings and cold chain logistics. In 2021, she received an R&D 100 Award for her contributions to Shadow Figment, a cybersecurity technology designed to defend critical infrastructure, such as buildings and the electric grid, against cyberattacks.

Beyond her research and innovation, Vrabie has led many workshops on reinforcement learning and control, scientific machine learning, and AI-driven decision-making. She was a plenary speaker at the 2022 American Control Conference (ACC), where she presented her work on integrating AI and control for next-generation autonomous systems.

Vrabie is the recipient of numerous awards. In 2022, she received the Most-Cited Article Award from the Annual Reviews in Control Journal for articles published after 2019. In 2021, she received the Best Paper Award from the Journal of Building Performance Simulation. In 2020, she received the Best Paper Award from the Energy Systems Technical Committee of the American Society of Mechanical Engineers at the Dynamic Systems and Control Conference. In 2017, she was named “Engineer of the Year” by the Women in Engineering section of the Institute of Electrical and Electronics Engineers (IEEE) in Richland, WA. In 2013, she received the Operational Excellence Award, and in 2012, she received an Outstanding Achievement Award from the United Technologies Research Center. In 2010, she received the Best Paper Award at the International Joint Conference on Neural Networks. In 2004, she received the Best Paper Award in the Advanced Control, Modeling and Simulation Session, International Conference on Automation, Quality and Testing, Robotics.

• Control Systems Theory
• Reinforcement Learning
• Model Based Predictive Control
• Optimization; Game Theory
• Deep Learning
• Scientific Machine Learning
• Dynamic systems
• Autonomous systems
• Robotics

• The University of Texas at Arlington
  Doctor of Philosophy, Electrical Engineering
• Gheorghe Asachi Technical University of Iasi
  Master of Engineering, Advanced Tech Studies
• Gheorghe Asachi Technical University of Iasi
  Bachelor of Science, Engineering Systems

• IEEE

• 2022 Most-Cited Article Award from the Annual Reviews in Control Journal, for articles published after 2019. 
• Best Paper Award from the Journal of Building Performance Simulation, 2021.
• R&D100 Award, 2021
• Best Paper Award, Energy Systems Technical Committee, ASME Dynamic Systems and Control Virtual Conference, 2020
• Engineer of the Year, IEEE Richland Section, Women in Engineering, 2017
• Operational Excellence Award, for setup of high-performance building testbed and demonstration of advanced applications, United Technologies Research Center Corporate Award, 2013
• Outstanding Achievement Award, for demonstration of advanced energy efficient buildings controls, United Technologies Research Center, 2012
• Best Paper Award, International Joint Conference on Neural Networks, Barcelona, Spain, 2010
• Outstanding Accomplishments, Department of Electrical Engineering, University of Texas at Arlington, 2009
• Automation and Robotics Research Institute Best Student Award, for distinguished scholarly accomplishments and outstanding service to pears and to the Automation and Robotics Research Institute community at large, 2009
• STEM Scholarship, University of Texas at Arlington, 2006 – 2009
• Best Paper Award in Advanced Control, Modeling and Simulation Session, International Conference on Automation, Quality and Testing, Robotics, 2004

A full list of Draguna Vrabie’s publications can be found on her Google Scholar profile. 

2025

• Huang W., N.M. Isenberg, J. Drgona, D.L. Vrabie, and B. Dilkina. 2025. "Efficient Primal Heuristics for Mixed Binary Quadratic Programs Using Suboptimal Rounding Guidance." In Proceedings of the 18th International Symposium on Combinatorial Search, August 12-15, 2025, Glasgow, UK, edited by M. Likhachev, H. Rudova and E. Scala, 18, 74-82. Palo Alto, California:Association for the Advancement of Artificial Intelligence. PNNL-SA-210182. doi:10.1609/socs.v18i1.35978

2024

• Drgona J., A.R. Tuor, and D.L. Vrabie. 2024. "Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees." IEEE Transactions on Systems, Man, and Cybernetics: Systems 54, no. 6:3596 - 3607. PNNL-SA-162577. doi:10.1109/TSMC.2024.3368026
• Shuvo S.S., S. Mukherjee, S. Chatterjee, S. Glavaski-Radovanovic, D.L. Vrabie, G. Canayon, and M. Juckes, et al. 2024. "Deep Multi-Agent Reinforcement Learning for Real-World Signalized Traffic Corridor Control." In IEEE International Conference on Machine Learning and Applications (ICMLA 2024), December 18-20, 2024, Miami, FL, 644-651. Piscataway, New Jersey:IEEE. PNNL-SA-202823. doi:10.1109/ICMLA61862.2024.00093

2023

• Bakker C., S.S. Vasisht, S. Huang, and D.L. Vrabie. 2023. "Sensor and Actuator Attacks on Hierarchical Control Systems with Domain-Aware Operator Theory." In Resilience Week (RWS 2023), November 27-30, 2023, National Harbor, MD, 1-8. Piscataway, New Jersey:IEEE. PNNL-SA-184521. doi:10.1109/RWS58133.2023.10284668
• Drgona J., A.R. Tuor, J.V. Koch, M.R. Shapiro, E. King, and D.L. Vrabie. 2023. Domain Aware Deep-learning Algorithms Integrated with Scientific-computing Technologies (DADAIST). PNNL-34895. Richland, WA: Pacific Northwest National Laboratory. Domain Aware Deep-learning Algorithms Integrated with Scientific-computing Technologies (DADAIST)
• Faulkner C.A., R.G. Lutes, S. Huang, W. Zuo, and D.L. Vrabie. 2023. "Simulation-based Assessment of ASHRAE Guideline 36, Considering Energy Performance, Indoor Air Quality, and Control Stability." Building and Environment 240. PNNL-SA-180891. doi:10.1016/j.buildenv.2023.110371
• Harouaka K., R.E. Richardson, E.C. Glasscock, A.D. French, I.J. Arnquist, E.W. Hoppe, and S.M. Akers, et al. 2023. Development of a hybrid neural network and transfer learning model for optimized ICP-MS/MS operation. PNNL-33377. Richland, WA: Pacific Northwest National Laboratory. Development of a hybrid neural network and transfer learning model for optimized ICP-MS/MS operation
• Huang S., R.G. Lutes, C.A. Faulkner, D.L. Vrabie, S. Katipamula, and W. Zuo. 2023. "An Open-Source Framework for Simulation-based Testing of Buildings Control Strategies." Journal of Building Performance Simulation 16, no. 6:631-643. PNNL-ACT-SA-10735. doi:10.1080/19401493.2023.2191220
• Koch J.V., Z. Chen, A.R. Tuor, J. Drgona, and D.L. Vrabie. 2023. "Structural Inference of Networked Dynamical Systems with Universal Differential Equations." Chaos: An Interdisciplinary Journal of Nonlinear Science 33, no. 2:023103. PNNL-SA-174936. doi:10.1063/5.0109093
• Nagy Z., G. Henze, S. Dey, J. Arroyo, L. Helsen, X. Zhang, and B. Chen, et al. 2023. "Ten questions concerning reinforcement learning for building energy management." Building and Environment 241. PNNL-SA-186171. doi:10.1016/j.buildenv.2023.110435
• Nghiem T., J. Drgona, C. Jones, Z. Nagy, R. Schwan, B. Dey, and A. Chakrabarty, et al. 2023. "Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems." In American Control Conference (ACC 2023), May 31-June 2, 2023, San Diego, CA, 3735-3750. Piscataway, New Jersey:IEEE. PNNL-SA-183234. doi:10.23919/ACC55779.2023.10155901
• Shaw Cortez W.E., S.S. Vasisht, A.R. Tuor, J. Drgona, and D.L. Vrabie. 2023. "Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles." In 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022), January 4-6, 2023, Canberra, Australia. IFAC-PapersOnline, edited by J. Trumpf and R. Mahony, 56, 228-233. Amsterdam:Elsevier. PNNL-SA-175459. doi:10.1016/j.ifacol.2023.02.039
• Soudbakhsh D., A.M. Annaswamy, Y. Wang, S. Brunton, J. Gaudio, H. Hussain, and D.L. Vrabie, et al. 2023. "Data-Driven Control: Theory and Applications." In Proceedings of the American Control Conference (ACC 2023), May 31- June 2, 2023, San Diego, CA, 1922-1939. Piscataway, New Jersey:IEEE. PNNL-SA-183238. doi:10.23919/ACC55779.2023.10156081
• Ye Y., C.A. Faulkner, R. Xu, S. Huang, Y. Liu, D.L. Vrabie, and J. Zhang, et al. 2023. "System Modeling for Grid-Interactive Efficient Building Applications." Journal of Building Engineering 69. PNNL-SA-174884. doi:10.1016/j.jobe.2023.106148

2022

• Bakker C., A. August, S. Huang, S.S. Vasisht, and D.L. Vrabie. 2022. "Deception-Based Cyber Attacks on Hierarchical Control Systems using Domain-Aware Koopman Learning." In Resilience Week (RWS 2022), September 26-29, 2022, National Harbor, MD, 1-8. Piscataway, New Jersey:IEEE. PNNL-SA-173605. doi:10.1109/RWS55399.2022.9984030
• Bhattacharya A., X. Ma, and D.L. Vrabie. 2022. "Model Predictive Control of Discrete-Continuous Energy Systems via Generalized Disjunctive Programming." In Modeling, Estimation and Control Conference, (MECC 2021), October 24-27, 2021, Austin, TX. IFAC-PapersOnLine, edited by J. Wang, et al, 54, 913-918. Amsterdam:Elsevier. PNNL-SA-148227. doi:10.1016/j.ifacol.2021.11.288
• Chen Y., J. Lerond, X. Lei, M.I. Rosenberg, and D.L. Vrabie. 2022. "A Knowledge-based Framework for Building Energy Model Performance Verification." In Proceedings of Building Simulation 2021: 17th Conference of IBPSA, September 1-3, 2021, Bruges, Belgium, edited by D. Saelens, et al, 17, 1943-1950. PNNL-SA-159510. doi:10.26868/25222708.2021.30725
• Drgona J., A.R. Tuor, S.S. Vasisht, and D.L. Vrabie. 2022. "Dissipative Deep Neural Dynamical Systems." IEEE Open Journal of Control Systems 1. PNNL-SA-174718. doi:10.1109/OJCSYS.2022.3186838
• Drgona J., K. Kis, A.R. Tuor, D.L. Vrabie, and M. Klauco. 2022. "Differentiable Predictive Control: Deep Learning Alternative to Explicit Model Predictive Control for Unknown Nonlinear Systems." Journal of Process Control 116, no. _:80-92. PNNL-SA-162137. doi:10.1016/j.jprocont.2022.06.001
• Drgona J., S. Mukherjee, A.R. Tuor, M. Halappanavar, and D.L. Vrabie. 2022. "Learning Stochastic Parametric Differentiable Predictive Control Policies." In 10th IFAC Symposium on Robust Control Design (ROCOND 2022), August 30 - September 2, 2022, Kyoto, Japan. IFAC-PapersOnLine, 55, 121 - 126. Amsterdam:Elsevier. PNNL-SA-170144. doi:10.1016/j.ifacol.2022.09.334
• King E., J. Drgona, A.R. Tuor, S.G. Abhyankar, C. Bakker, A. Bhattacharya, and D.L. Vrabie. 2022. "Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem." In American Control Conference (ACC 2022), June 8-10, 2022, Atlanta, GA, 2194-2201. Piscataway, New Jersey:IEEE. PNNL-SA-167421. doi:10.23919/ACC53348.2022.9867379
• Mukherjee S., J. Drgona, A.R. Tuor, M. Halappanavar, and D.L. Vrabie. 2022. "Neural Lyapunov Differentiable Predictive Control." In Proceedings of the 61st IEEE Conference on Decision and Control (CDC 2022), December 6-9, 2022, Cancun, Mexico, 2097-2104. Piscataway, New Jersey:IEEE. PNNL-SA-171659. doi:10.1109/CDC51059.2022.9992386
• Mukherjee S., S. Nandanoori, S. Guan, K. Agarwal, S. Sinha, S. Kundu, and S. Pal, et al. 2022. "Learning Distributed Geometric Koopman Operator for Sparse Networked Dynamical Systems." In First Learning on Graphs Conference (LoG 2022), December 9-12, 2022, Virtual, Online. Proceedings of Machine Learning Research, 198, 1-17. Maastricht:ML Research Press. PNNL-SA-173554.
• Nandanoori S., S. Kundu, J. Lian, U. Vaidya, D.L. Vrabie, and K. Kalsi. 2022. "Sparse Control Synthesis for Uncertain Responsive Loads with Stochastic Stability Guarantees." IEEE Transactions on Power Systems 37, no. 1:167 - 178. PNNL-SA-156076. doi:10.1109/TPWRS.2021.3095827
• Shaw Cortez W.E., J. Drgona, A.R. Tuor, M. Halappanavar, and D.L. Vrabie. 2022. "Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach." In Proceedings of the 61th IEEE Conference on Decision and Control (CDC 2022), December 6-9, 2022, Cancun, Mexico, 932-938. Piscataway, New Jersey:IEEE. PNNL-SA-171767. doi:10.1109/CDC51059.2022.9993146
• Vrabie D.L. 2022. Dynamic Decarbonization through Autonomous Physics-Centric Deep Learning and Optimization of Building Operations - CRADA 552. PNNL-32956. Richland, WA: Pacific Northwest National Laboratory. Dynamic Decarbonization through Autonomous Physics-Centric Deep Learning and Optimization of Building Operations - CRADA 552

2021

• Bakker C., A. Bhattacharya, S. Chatterjee, and D.L. Vrabie. 2021. "Metagames and Hypergames for Deception-Robust Control." ACM Transactions on Cyber-Physical Systems 5, no. 3:1-25. PNNL-SA-147800. doi:10.1145/3439430
• Bhattacharya A., S.S. Vasisht, V.A. Adetola, S. Huang, H. Sharma, and D.L. Vrabie. 2021. "Control Co-Design of Commercial Building Chiller Plant using Bayesian Optimization." Energy and Buildings 246. PNNL-SA-158439. doi:10.1016/j.enbuild.2021.111077
• Blum D., J. Arroyo, S. Huang, J. Drgona, F. Jorissen, H. Walnum, and Y. Chen, et al. 2021. "Building Optimization Testing Framework (BOPTEST) for Simulation-Based Benchmarking of Control Strategies in Buildings." Journal of Building Performance Simulation 14, no. 5:586-610. PNNL-SA-164986. doi:10.1080/19401493.2021.1986574
• Drgona J., A.R. Tuor, S.E. Skomski, S.S. Vasisht, and D.L. Vrabie. 2021. "Deep Learning Explicit Differentiable Predictive Control Laws for Buildings." In 7th IFAC Conference on Nonlinear Model Predictive Control, (NMPC 2021), July 11-14, 2021 Bratislava. IFAC-PapersOnLine, edited by G. Pannocchia, et al, 54, 4-19. PNNL-SA-159943. doi:10.1016/j.ifacol.2021.08.518
• Drgona J., A.R. Tuor, V. Chandan, and D.L. Vrabie. 2021. "Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics." Energy and Buildings 243. PNNL-SA-166470. doi:10.1016/j.enbuild.2021.110992
• Huang S., J. Wang, Y. Fu, W. Zuo, K. Hinkelman, R. Kaiser, and D. He, et al. 2021. "An Open-Source Virtual Testbed for a Real Net-Zero Energy Community." Sustainable Cities and Society 75. PNNL-SA-143454. doi:10.1016/j.scs.2021.103255
• Huang S., W. Zuo, D.L. Vrabie, and R. Xu. 2021. "Modelica-based system modeling for studying control-related faults in chiller plants and boiler plants serving large office buildings." Journal of Building Engineering 44. PNNL-SA-158961. doi:10.1016/j.jobe.2021.102654
• Jain M., S. Kundu, A. Bhattacharya, S. Huang, V. Chandan, N. Radhakrishnan, and V.A. Adetola, et al. 2021. "Occupancy-Driven Stochastic Decision Framework for Ranking Commercial Building Loads." In Proceedings of the American Control Conference (ACC 2021), May 25-28, 2021, Virtual, New Orleans, LA, 2021, 4171 - 4177. Piscataway, New Jersey:IEEE. PNNL-SA-156937. doi:10.23919/ACC50511.2021.9482639
• Kundu S., A. Bhattacharya, V. Chandan, N. Radhakrishnan, V.A. Adetola, and D.L. Vrabie. 2021. "A Stochastic Multi-Criteria Decision-Making Algorithm for Dynamic Load Prioritization in Grid-Interactive Efficient Buildings." ASME Letters in Dynamic Systems and Control 1, no. 3:Article No. 031014. PNNL-SA-156073. doi:10.1115/1.4050124
• Nowak K.E., J.M. Brandi, W.J. Hofer, T.W. Edgar, and D.L. Vrabie. 2021. "Data-driven model generation for deception defence of cyber-physical environments." Journal of Information Warfare 20, no. 2:27-41. PNNL-SA-155275.
• Skomski E., S.S. Vasisht, C.L. Wight, A.R. Tuor, J. Drgona, and D.L. Vrabie. 2021. "Constrained Block Nonlinear Neural Dynamical Models." In Proceedings of the American Control Conference (ACC 2021), May 25-28, 2021, Virtual, New Orleans, LA, 2021, 3993 - 4000. Piscataway, New Jersey:IEEE. PNNL-SA-156893. doi:10.23919/ACC50511.2021.9482930
• Wang J., S. Huang, W. Zuo, and D.L. Vrabie. 2021. "Occupant Preference-Aware Load Scheduling for Resilient Communities." Energy and Buildings 252. PNNL-SA-162485. doi:10.1016/j.enbuild.2021.111399

2020

• Bakker C., A. Bhattacharya, S. Chatterjee, and D.L. Vrabie. 2020. "Hypergames and Cyber-Physical Security for Control Systems." ACM Transactions on Cyber-Physical Systems 4, no. 4:45. PNNL-SA-144077. doi:10.1145/3384676
• Bhattacharya S., Y. Chen, S. Huang, and D.L. Vrabie. 2020. "A Learning-based time-efficient framework for building energy performance evaluation." Energy and Buildings 228. PNNL-SA-152322. doi:10.1016/j.enbuild.2020.110411
• Chakraborty I., Y. Chen, J. Li, and D.L. Vrabie. 2020. "Theoretical Development of Controller Transfer applied to Dynamical Systems." In Proceedings of the 11th ACM International Conference on Future Energy Systems (e-Energy 2020), June 22-26, 2020, 445-453. New York, New York:Association for Computing Machinery. PNNL-SA-152583. doi:10.1145/3396851.3402368
• Drgona J., A.R. Tuor, V. Chandan, and D.L. Vrabie. 2020. "Physics-constrained Deep Recurrent Neural Models of Building Thermal Dynamics." In Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop Tackling Climate Change with Machine Learning, December 11, 2020, Virtual. San Diego, California:Neural Information Processing Systems Foundation, Inc. PNNL-SA-156966.
• Drgona J., J. Arroyo, I.C. Figueroa, D. Blum, K. Arendt, D. Kim, and E.P. Oll�, et al. 2020. "All you need to know about model predictive control for buildings." Annual Reviews in Control 50. PNNL-SA-153009. doi:10.1016/j.arcontrol.2020.09.001
• Drgona J., L. Helsen, and D.L. Vrabie. 2020. "Cutting the Deployment Costs of Physics-Based MPC in Buildings by Simulation-Based Imitation Learning." In ASME 2020 Dynamic Systems and Control Conference, October 5-7, 2020, Virtual, Online, 1, DSCC2020-3118, V001T09A001. New York, New York:ASME. PNNL-SA-147855. doi:10.1115/DSCC2020-3118
• Newman S.F., J. Lerond, H.M. Reeve, D.L. Vrabie, S.T. Belew, J.C. Tucker, and E.T. Mayhorn. 2020. "Pilot Study for Determining HVAC Fault Prevalence from Fault Monitoring Data." In ACEEE Summer Study on Energy Efficiency in Buildings, August 17- 21, 2020. Virtual, Online, 3-244 - 3-258. Washington Dc:American Council for an Energy-Efficient Economy. PNNL-SA-152014.
• Wang J., K. Garifi, K. Baker, W. Zuo, Y. Zhang, S. Huang, and D.L. Vrabie. 2020. "Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities." Energies 13, no. 21:5683. PNNL-SA-156963. doi:10.3390/en13215683
• Wang S., R. Huang, X. Ke, J. Zhao, R. Fan, H. Wang, and Z. Huang, et al. 2020. "Risk-Oriented PMU Placement Approach in Electric Power Systems." IET Generation, Transmission and Distribution 14, no. 2:301-307. PNNL-SA-128708. doi:10.1049/iet-gtd.2019.0957

2019

• Abhyankar S.G., S. Peles, and D.L. Vrabie. 2019. Power Grid Computational Challenges and Metrics for Hardware Accelerator Evaluation. PNNL-29301. Richland, WA: Pacific Northwest National Laboratory. Power Grid Computational Challenges and Metrics for Hardware Accelerator Evaluation
• Bhattacharya A., S. Bopardikar, S. Chatterjee, and D.L. Vrabie. 2019. "Cyber Threat Screening Using a Queuing-Based Game-Theoretic Approach." Journal of Information Warfare 18, no. 4 (Special Edition):37-52. PNNL-SA-145431.
• Chakraborty I., V. Chandan, and D.L. Vrabie. 2019. "A sequential DNN based Baseline Energy Prediction Framework with Long term Error Mitigation." In Proceedings of the Tenth ACM International Conference on Future Energy Systems (e-Energy 2019), June 25-28, 2019, Phoenix, AZ, 508-515. New York, New York:ACM. PNNL-SA-141775. doi:10.1145/3307772.3331027
• Chen Y., A. Bhattacharya, J. Li, and D.L. Vrabie. 2019. Optimal Control by Transfer-Learning. PNNL-29168. Richland, WA: Pacific Northwest National Laboratory. Optimal Control by Transfer-Learning
• Dong J., T. Ramachandran, P. Im, S. Huang, V. Chandan, D.L. Vrabie, and P.V. Kuruganti. 2019. "Online Learning for Commercial Buildings." In Proceedings of the Tenth ACM International Conference on Future Energy Systems (e-Energy 2019), June 25-28, 2019, Phoenix, AZ, 522-530. New York, New York:ACM. PNNL-SA-138212. doi:10.1145/3307772.3331029
• Drgona J., L. Helsen, and D.L. Vrabie. 2019. "Stripping off the implementation complexity of physics-based model predictive control for buildings via deep learning." In 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), December 8-14, 2019,Vancouver, Canada.. Vancouver:Neural Information Processing Systems. PNNL-SA-147272.
• Fu Y., S. Huang, D.L. Vrabie, and W. Zuo. 2019. "Coupling Power System Dynamics and Building Dynamics to Enable Building-to-Grid Integration." In Proceedings of the13th International Modelica Conference, march 4-6, 2019, Regensburg, Germany. Linköping:Linköping University Electronic Press. PNNL-SA-139720. doi:10.3384/ecp19157561
• Hofer W.J., T.W. Edgar, D.L. Vrabie, and K.E. Nowak. 2019. "Model-driven Deception for Control System Environments." In IEEE Symposium on Technologies for Homeland Security (HST2019), November 5-6, 2019, Woburn, MA, 1-7. Piscataway, New Jersey:IEEE. PNNL-SA-143715. doi:10.1109/HST47167.2019.9032927

2018

• Chakraborty I., and D.L. Vrabie. 2018. "Fault Detection for Dynamical Systems using Differential Geometric and Concurrent Learning Approach." IFAC - PapersOnLine 51, no. 24:1395-1402. PNNL-SA-129423. doi:10.1016/j.ifacol.2018.09.552
• Chakraborty I., R. Chakraborty, and D.L. Vrabie. 2018. "Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems." In 29th International Workshop on Principles of Diagnosis (DX'18), August 27-30, 2018, Warsaw, Poland, edited by L Trave-Massuyes and A Sztyber, 2289. Aachen:Aachen RWTH. PNNL-SA-134059.
• Huang S., Y. Chen, P.W. Ehrlich, and D.L. Vrabie. 2018. "A Control-oriented Building Envelope and HVAC System Simulation Model for a Typical Large Office Building." In 2018 Building Performance Analysis Conference and SimBuild co-organized by ASHRAE and IBPSA-USA, September 26-28, 2018, Chicago, IL, 729-736. Atlanta, Georgia:American Society of Heating, Refrigerating and Air-Conditioning Engineers. PNNL-SA-132275.
• Kundu S., T. Ramachandran, Y. Chen, and D.L. Vrabie. 2018. "Optimal Energy Consumption Forecast for Grid Responsive Buildings: A Sensitivity Analysis." In IEEE Conference on Control Technology and Applications (CCTA 2018), August 21-24, 2018, Copenhagen, Denmark, 230-236. Piscataway, New Jersey:IEEE. PNNL-SA-132105. doi:10.1109/CCTA.2018.8511607
• Rubio-Herrero J., V. Chandan, C.M. Siegel, A. Vishnu, and D.L. Vrabie. 2018. "A Learning Framework for Control-Oriented Modeling of Buildings." In IEEE International Conference on Machine Learning and Applications (ICMLA 2017) December 18-21, 2017, Cancun, Mexico. Piscataway, New Jersey:IEEE. PNNL-SA-128813. doi:10.1109/ICMLA.2017.00079

2017

• Ramachandran T., S. Kundu, Y. Chen, and D.L. Vrabie. 2017. "Towards a framework for selection of supervisory control for commercial buildings: HVAC system energy efficiency." In American Control Conference (ACC 2017), May 24-26, 2017, Seattle, Washington, 2925-2930. Piscataway, New Jersey:IEEE. PNNL-SA-121108. doi:10.23919/ACC.2017.7963395
• Ramachandran T., S. Kundu, Y. Chen, and D.L. Vrabie. 2017. "Towards a framework for selection of supervisory control for commercial buildings: HVAC system energy efficiency." In American Control Conference (ACC 2017), May 24-26, 2017, Seattle, Washington, 2925-2930. Piscataway, New Jersey:IEEE. PNNL-SA-124310. doi:10.23919/ACC.2017.7963395