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Ján Drgona
Person information
- unicode name: Ján Drgoňa
- affiliation: Pacific Northwest National Laboratory (PNNL), Richland, WA, USA
- affiliation: KU Leuven, Belgium
- affiliation (PhD): Slovak University of Technology, Bratislava, Slovakia
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2020 – today
- 2024
- [j6]Karthik Somayaji N. S., Yu Wang, Malachi Schram, Ján Drgona, Mahantesh M. Halappanavar, Frank Liu, Peng Li:
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory. Trans. Mach. Learn. Res. 2024 (2024) - [j5]Ján Drgona
, Aaron Tuor, Draguna L. Vrabie
Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees. IEEE Trans. Syst. Man Cybern. Syst. 54(6): 3596-3607 (2024) - [c19]Yu Wang, Yuxuan Yin, Karthik Somayaji N. S., Ján Drgona, Malachi Schram, Mahantesh Halappanavar, Frank Liu, Peng Li:
Semi-supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach. AAAI 2024: 15698-15705 - [i30]James Koch, Madelyn Shapiro, Himanshu Sharma, Draguna L. Vrabie, Ján Drgona:
Neural Differential Algebraic Equations. CoRR abs/2403.12938 (2024) - [i29]Ethan King, James Kotary, Ferdinando Fioretto, Ján Drgona:
Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming. CoRR abs/2404.00882 (2024) - [i28]Renukanandan Tumu, Wenceslao Shaw-Cortez, Ján Drgona, Draguna L. Vrabie, Sonja Glavaski:
Differentiable Predictive Control for Large-Scale Urban Road Networks. CoRR abs/2406.10433 (2024) - [i27]John Viljoen, Wenceslao Shaw-Cortez, Ján Drgona, Sebastian East, Masayoshi Tomizuka, Draguna L. Vrabie:
Differentiable Predictive Control for Robotics: A Data-Driven Predictive Safety Filter Approach. CoRR abs/2409.13817 (2024) - 2023
- [j4]Christian Møldrup Legaard
Constructing Neural Network Based Models for Simulating Dynamical Systems. ACM Comput. Surv. 55(11): 236:1-236:34 (2023) - [c18]Yu Wang, Ján Drgona, Jiaxin Zhang, Karthik Somayaji Nanjangud Suryanarayana, Malachi Schram, Frank Liu, Peng Li:
AutoNF: Automated Architecture Optimization of Normalizing Flows with Unconstrained Continuous Relaxation Admitting Optimal Discrete Solution. AAAI 2023: 10244-10252 - [c17]Damoon Soudbakhsh, Anuradha M. Annaswamy, Yan Wang, Steven L. Brunton, Joseph E. Gaudio, Heather S. Hussain, Draguna L. Vrabie, Ján Drgona, Dimitar P. Filev:
Data-Driven Control: Theory and Applications. ACC 2023: 1922-1939 - [c16]Truong X. Nghiem
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems. ACC 2023: 3735-3750 - [c15]Shimiao Li
Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning. e-Energy (Companion) 2023: 106-114 - [i26]Shimiao Li, Ján Drgona, Shrirang Abhyankar
Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning. CoRR abs/2304.10702 (2023) - [i25]Truong X. Nghiem, Ján Drgona, Colin N. Jones, Zoltán Nagy, Roland Schwan, Biswadip Dey, Ankush Chakrabarty, Stefano Di Cairano, Joel A. Paulson, Andrea Carron, Melanie N. Zeilinger, Wenceslao Shaw-Cortez, Draguna L. Vrabie:
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems. CoRR abs/2306.13867 (2023) - [i24]Karthik Somayaji N. S., Yu Wang, Malachi Schram
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory. CoRR abs/2308.13011 (2023) - [i23]Yu Wang, Yuxuan Yin, Karthik Somayaji Nanjangud Suryanarayana, Ján Drgona, Malachi Schram, Mahantesh Halappanavar, Frank Liu, Peng Li:
Semi-Supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach. CoRR abs/2310.13110 (2023) - [i22]Wenceslao Shaw-Cortez, Ján Drgona, Draguna L. Vrabie, Mahantesh Halappanavar:
Robust Differentiable Predictive Control with Safety Guarantees: A Predictive Safety Filter Approach. CoRR abs/2311.08496 (2023) - 2022
- [c14]Ethan King
Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem. ACC 2022: 2194-2201 - [c13]Aowabin Rahman, Ján Drgona, Aaron Tuor, Jan Strube
Neural Ordinary Differential Equations for Nonlinear System Identification. ACC 2022: 3979-3984 - [c12]Wenceslao Shaw-Cortez, Ján Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie
Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach. CDC 2022: 932-938 - [c11]Sayak Mukherjee, Ján Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie
Neural Lyapunov Differentiable Predictive Control. CDC 2022: 2097-2104 - [c10]Subhrajit Sinha, Sai Pushpak Nandanoori, Ján Drgona, Draguna L. Vrabie
Data-driven Stabilization of Discrete-time Control-affine Nonlinear Systems: A Koopman Operator Approach. ECC 2022: 552-559 - [i21]Aowabin Rahman, Ján Drgona, Aaron Tuor, Jan Strube
Neural Ordinary Differential Equations for Nonlinear System Identification. CoRR abs/2203.00120 (2022) - [i20]Ján Drgona, Sayak Mukherjee, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie:
Learning Stochastic Parametric Differentiable Predictive Control Policies. CoRR abs/2203.01447 (2022) - [i19]Ethan King, Ján Drgona, Aaron Tuor, Shrirang Abhyankar
Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem. CoRR abs/2203.08984 (2022) - [i18]Shrirang Abhyankar
Neuro-physical dynamic load modeling using differentiable parametric optimization. CoRR abs/2203.10582 (2022) - [i17]Subhrajit Sinha, Sai Pushpak Nandanoori, Ján Drgona, Draguna L. Vrabie:
Data-driven Stabilization of Discrete-time Control-affine Nonlinear Systems: A Koopman Operator Approach. CoRR abs/2203.14114 (2022) - [i16]Sayak Mukherjee, Ján Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie:
Neural Lyapunov Differentiable Predictive Control. CoRR abs/2205.10728 (2022) - [i15]James Koch, Zhao Chen, Aaron Tuor, Ján Drgona, Draguna L. Vrabie:
Structural Inference of Networked Dynamical Systems with Universal Differential Equations. CoRR abs/2207.04962 (2022) - [i14]Wenceslao Shaw-Cortez, Ján Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie:
Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach. CoRR abs/2208.02319 (2022) - [i13]Wenceslao Shaw-Cortez, Soumya Vasisht
Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles. CoRR abs/2208.07333 (2022) - [i12]Hari Prasanna Das, Yu-Wen Lin, Utkarsha Agwan, Lucas Spangher, Alex Devonport, Yu Yang, Ján Drgona, Adrian Chong, Stefano Schiavon, Costas J. Spanos:
Machine Learning for Smart and Energy-Efficient Buildings. CoRR abs/2211.14889 (2022) - [i11]Feras A. Batarseh, Priya L. Donti, Ján Drgona, Kristen Fletcher, Pierre-Adrien Hanania, Melissa Hatton, Srinivasan Keshav, Bran Knowles, Raphaela Kotsch, Sean McGinnis, Peetak Mitra, Alex Philp, Jim Spohrer, Frank Stein, Meghna Tare, Svitlana Volkov, Gege Wen:
Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges. CoRR abs/2212.13631 (2022) - 2021
- [c9]Elliott Skomski, Soumya Vasisht
Constrained Block Nonlinear Neural Dynamical Models. ACC 2021: 3993-4000 - [c8]Elliott Skomski, Ján Drgona, Aaron Tuor:
Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution. L4DC 2021: 980-991 - [c7]Kingsley Nweye, Zoltán Nagy, Sharada P. Mohanty, Dipam Chakraborty, Siva Sankaranarayanan, Tianzhen Hong, Sourav Dey, Gregor Henze, Ján Drgona, Fangquan Lin, Wei Jiang, Hanwei Zhang, Zhongkai Yi, Jihai Zhang, Cheng Yang, Matthew Motoki, Sorapong Khongnawang, Michael Ibrahim, Abilmansur Zhumabekov, Daniel May, Zhihu Yang, Xiaozhuang Song, Han Zhang, Xiaoning Dong, Shun Zheng, Jiang Bian:
The CityLearn Challenge 2022: Overview, Results, and Lessons Learned. NeurIPS (Competition and Demos) 2021: 85-103 - [c6]Ján Drgona, Sayak Mukherjee, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar:
On the Stochastic Stability of Deep Markov Models. NeurIPS 2021: 24033-24047 - [c5]Zoltán Nagy, June Young Park, Ján Drgona, Matias Quintana:
The second ACM SIGEnergy workshop on reinforcement learning for energy management in buildings & cities (RLEM). BuildSys@SenSys 2021: 307-308 - [i10]Elliott Skomski, Soumya Vasisht, Colby Wight, Aaron Tuor, Ján Drgona, Draguna L. Vrabie:
Constrained Block Nonlinear Neural Dynamical Models. CoRR abs/2101.01864 (2021) - [i9]Ján Drgona, Aaron Tuor, Soumya Vasisht, Elliott Skomski, Draguna L. Vrabie:
Deep Learning Explicit Differentiable Predictive Control Laws for Buildings. CoRR abs/2107.11843 (2021) - [i8]Christian Møldrup Legaard, Thomas Schranz, Gerald Schweiger, Ján Drgona, Basak Falay, Cláudio Gomes, Alexandros Iosifidis, Mahdi Abkar, Peter Gorm Larsen:
Constructing Neural Network-Based Models for Simulating Dynamical Systems. CoRR abs/2111.01495 (2021) - [i7]Ján Drgona, Sayak Mukherjee, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar:
On the Stochastic Stability of Deep Markov Models. CoRR abs/2111.04601 (2021) - 2020
- [j3]Ján Drgona, Javier Arroyo
All you need to know about model predictive control for buildings. Annu. Rev. Control. 50: 190-232 (2020) - [i6]Aaron Tuor, Ján Drgona, Draguna L. Vrabie
Constrained Neural Ordinary Differential Equations with Stability Guarantees. CoRR abs/2004.10883 (2020) - [i5]Ján Drgona, Aaron Tuor, Draguna L. Vrabie
Constrained Physics-Informed Deep Learning for Stable System Identification and Control of Unknown Linear Systems. CoRR abs/2004.11184 (2020) - [i4]Ján Drgona, Karol Kis, Aaron Tuor, Draguna L. Vrabie, Martin Klauco:
Differentiable Predictive Control: An MPC Alternative for Unknown Nonlinear Systems using Constrained Deep Learning. CoRR abs/2011.03699 (2020) - [i3]Ján Drgona, Aaron Randall Tuor, Vikas Chandan, Draguna L. Vrabie:
Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics. CoRR abs/2011.05987 (2020) - [i2]Ján Drgona, Elliott Skomski, Soumya Vasisht, Aaron Tuor, Draguna L. Vrabie:
Spectral Analysis and Stability of Deep Neural Dynamics. CoRR abs/2011.13492 (2020) - [i1]Elliott Skomski, Ján Drgona, Aaron Tuor:
Physics-Informed Neural State Space Models via Learning and Evolution. CoRR abs/2011.13497 (2020)
2010 – 2019
- 2018
- [j2]Juraj Holaza, Martin Klauco
MPC-based reference governor control of a continuous stirred-tank reactor. Comput. Chem. Eng. 108: 289-299 (2018) - 2017
- [j1]Ján Drgona
Optimal control of a laboratory binary distillation column via regionless explicit MPC. Comput. Chem. Eng. 96: 139-148 (2017) - 2016
- [c4]Ján Drgona
Regionless explicit MPC of a distillation column. ECC 2016: 1568-1573 - 2015
- [c3]Ján Drgona
MPC-based reference governors for thermostatically controlled residential buildings. CDC 2015: 1334-1339 - 2013
- [c2]Ján Drgona
Explicit stochastic MPC approach to building temperature control. CDC 2013: 6440-6445 - [c1]Michal Kvasnica, Alexander Szucs, Miroslav Fikar
Explicit MPC of LPV systems in the controllable canonical form. ECC 2013: 1035-1040
Coauthor Index
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last updated on 2024-10-16 20:33 CEST by the dblp team
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