Staff information

PNNL Publications

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
• Yang Z., A.D. GAIDHANE, J. Drgona, V. Chandan, M. Halappanavar, F. Liu, and Y. Cao. 2024. "Physics-constrained graph modeling for building thermal dynamics." Energy and AI 16. PNNL-SA-195071. doi:10.1016/j.egyai.2024.100346

2023

• Abhyankar S.G., J. Drgona, A.R. Tuor, and A.J. August. 2023. "Neuro-physical dynamic load modeling using differentiable parametric optimization." In IEEE Power & Energy Society General Meeting (PESGM 2023), July 16-20, 2023, Orlando, FL, 1-5. Piscataway, New Jersey:IEEE. PNNL-SA-179235. doi:10.1109/PESGM52003.2023.10253098
• 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)
• 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
• Legaard C.M., T. Schranz, G. Schweiger, J. Drgona, B. Falay, C. Gomes, and A. Iosifidis, et al. 2023. "Constructing Neural Network-Based Models for Simulating Dynamical Systems." ACM Computing Surveys 55, no. 11:Art. No. 236. PNNL-SA-168499. doi:10.1145/3567591
• Li S., J. Drgona, S.G. Abhyankar, and L. Pileggi. 2023. "Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning." In e-Energy '23 Companion: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems, June 20-23, 2023, Orlando, FL, 106-114. New York, New York:Association for Computing Machinery. PNNL-SA-184952. doi:10.1145/3599733.3600257
• 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

2022

• 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. 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
• Rahman A., J. Drgona, A.R. Tuor, and J.F. Strube. 2022. "Neural Ordinary Differential Equations for Nonlinear System Identification." In American Control Conference (ACC 2022), June 8-10, 2022, Atlanta, GA, 3979-3984. Piscataway, New Jersey:IEEE. PNNL-SA-167502. doi:10.23919/ACC53348.2022.9867586
• Schranz T., J. Exenberger, C.L. Møldrup, J. Drgona, and G. Schweiger. 2022. "Energy Prediction under Changed Demand Conditions: Robust Machine Learning Models and Input Feature Combinations." In Proceedings of Building Simulation 2021: 17th Conference of IBPSA, September 1-3, 2021, Bruges, Belgium, edited by D. Saelens, et al, 17, 3268 - 3275. PNNL-SA-159867. doi:10.26868/25222708.2021.30806
• 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

2021

• 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
• Drgona J., S. Mukherjee, J. Zhang, F. Liu, and M. Halappanavar. 2021. "On the Stochastic Stability of Deep Markov Models." In Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), December 6-14, 2021, Virtual, Online. Advances in Neural Information Processing Systems, edited by M. Ranzato, et al, 34. San Jose, California:Curran Associates Inc. PNNL-SA-162811.
• Figueroa I.C., M. Cimmino, J. Drgona, and L. Helsen. 2021. "Fluid temperature predictions of geothermal borefields using load estimations via state observers." Journal of Building Performance Simulation 14, no. 1:1-19. PNNL-SA-156303. doi:10.1080/19401493.2020.1838612
• 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
• Skomski S.E., J. Drgona, and A.R. Tuor. 2021. "Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution." In Proceedings of the 3rd Conference on Learning for Dynamics and Control, June 7-8, 2021, edited by A. Jababaie, et al, 144, 980--991. Brookline, Massachusetts:ML Research Press. PNNL-SA-158080.

2020

• 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., D. Picard, and L. Helsen. 2020. "Cloud-based implementation of white-box model predictive control for a GEOTABS office building: A field test demonstration." Journal of Process Control 88. PNNL-SA-152979. doi:10.1016/j.jprocont.2020.02.007
• Drgona J., J. Arroyo, I.C. Figueroa, D. Blum, K. Arendt, D. Kim, and E.P. Ollé, 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

2019

• 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.