Ján Drgona

According to our database, Ján Drgona authored at least 45 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees.

Ján Drgona, Aaron Tuor, Draguna L. Vrabie

IEEE Trans. Syst. Man Cybern. Syst., June, 2024

Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory.

Karthik Somayaji N. S., Yu Wang, Malachi Schram, Ján Drgona, Mahantesh M. Halappanavar, Frank Liu, Peng Li

Trans. Mach. Learn. Res., 2024

Learning to Optimize for Mixed-Integer Non-linear Programming.

Bo Tang, Elias B. Khalil, Ján Drgona

CoRR, 2024

Differentiable Predictive Control for Robotics: A Data-Driven Predictive Safety Filter Approach.

John Viljoen, Wenceslao Shaw-Cortez, Ján Drgona, Sebastian East, Masayoshi Tomizuka, Draguna L. Vrabie

CoRR, 2024

Differentiable Predictive Control for Large-Scale Urban Road Networks.

Renukanandan Tumu, Wenceslao Shaw-Cortez, Ján Drgona, Draguna L. Vrabie, Sonja Glavaski

CoRR, 2024

Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming.

Ethan King, James Kotary, Ferdinando Fioretto, Ján Drgona

CoRR, 2024

Neural Differential Algebraic Equations.

James Koch, Madelyn Shapiro, Himanshu Sharma, Draguna L. Vrabie, Ján Drgona

CoRR, 2024

Finding MIDDLE Ground: Scalable and Secure Distributed Learning.

Marco Bornstein, Nawaf Nazir, Ján Drgona, Soumya Kundu, Veronica Adetola

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

Semi-supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach.

Yu Wang, Yuxuan Yin, Karthik Somayaji N. S., Ján Drgona, Malachi Schram, Mahantesh Halappanavar, Frank Liu, Peng Li

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Constructing Neural Network Based Models for Simulating Dynamical Systems.

Christian Møldrup Legaard, Thomas Schranz, Gerald Schweiger, Ján Drgona, Basak Falay, Cláudio Gomes, Alexandros Iosifidis, Mahdi Abkar, Peter Gorm Larsen

ACM Comput. Surv., November, 2023

Robust Differentiable Predictive Control with Safety Guarantees: A Predictive Safety Filter Approach.

Wenceslao Shaw-Cortez, Ján Drgona, Draguna L. Vrabie, Mahantesh Halappanavar

CoRR, 2023

Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning.

Shimiao Li, Ján Drgona, Shrirang Abhyankar, Larry T. Pileggi

Proceedings of the Companion Proceedings of the 14th ACM International Conference on Future Energy Systems, 2023

Data-Driven Control: Theory and Applications.

Damoon Soudbakhsh, Anuradha M. Annaswamy, Yan Wang, Steven L. Brunton, Joseph E. Gaudio, Heather S. Hussain, Draguna L. Vrabie, Ján Drgona, Dimitar P. Filev

Proceedings of the American Control Conference, 2023

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems.

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

Proceedings of the American Control Conference, 2023

AutoNF: Automated Architecture Optimization of Normalizing Flows with Unconstrained Continuous Relaxation Admitting Optimal Discrete Solution.

Yu Wang, Ján Drgona, Jiaxin Zhang, Karthik Somayaji Nanjangud Suryanarayana, Malachi Schram, Frank Liu, Peng Li

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges.

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

CoRR, 2022

Machine Learning for Smart and Energy-Efficient Buildings.

Hari Prasanna Das, Yu-Wen Lin, Utkarsha Agwan, Lucas Spangher, Alex Devonport, Yu Yang, Ján Drgona, Adrian Chong, Stefano Schiavon, Costas J. Spanos

CoRR, 2022

Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles.

Wenceslao Shaw-Cortez, Soumya Vasisht, Aaron Tuor, Ján Drgona, Draguna L. Vrabie

CoRR, 2022

Structural Inference of Networked Dynamical Systems with Universal Differential Equations.

James Koch, Zhao Chen, Aaron Tuor, Ján Drgona, Draguna L. Vrabie

CoRR, 2022

Neuro-physical dynamic load modeling using differentiable parametric optimization.

Shrirang Abhyankar, Ján Drgona, Andrew August, Elliott Skomski, Aaron Tuor

CoRR, 2022

Learning Stochastic Parametric Differentiable Predictive Control Policies.

Ján Drgona, Sayak Mukherjee, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie

CoRR, 2022

Data-driven Stabilization of Discrete-time Control-affine Nonlinear Systems: A Koopman Operator Approach.

Subhrajit Sinha, Sai Pushpak Nandanoori, Ján Drgona, Draguna L. Vrabie

Proceedings of the European Control Conference, 2022

Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach.

Wenceslao Shaw-Cortez, Ján Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Neural Lyapunov Differentiable Predictive Control.

Sayak Mukherjee, Ján Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Neural Ordinary Differential Equations for Nonlinear System Identification.

Aowabin Rahman, Ján Drgona, Aaron Tuor, Jan Strube

Proceedings of the American Control Conference, 2022

Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem.

Ethan King, Ján Drgona, Aaron Tuor, Shrirang Abhyankar, Craig Bakker, Arnab Bhattacharya, Draguna L. Vrabie

Proceedings of the American Control Conference, 2022

2021

Deep Learning Explicit Differentiable Predictive Control Laws for Buildings.

Ján Drgona, Aaron Tuor, Soumya Vasisht, Elliott Skomski, Draguna L. Vrabie

CoRR, 2021

The second ACM SIGEnergy workshop on reinforcement learning for energy management in buildings & cities (RLEM).

Zoltán Nagy, June Young Park, Ján Drgona, Matias Quintana

Proceedings of the BuildSys '21: The 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Coimbra, Portugal, November 17, 2021

The CityLearn Challenge 2022: Overview, Results, and Lessons Learned.

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

Proceedings of the NeurIPS 2022 Competition Track, 2021

On the Stochastic Stability of Deep Markov Models.

Ján Drgona, Sayak Mukherjee, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution.

Elliott Skomski, Ján Drgona, Aaron Tuor

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Constrained Block Nonlinear Neural Dynamical Models.

Elliott Skomski, Soumya Vasisht, Colby Wight, Aaron Tuor, Ján Drgona, Draguna L. Vrabie

Proceedings of the 2021 American Control Conference, 2021

2020

Physics-Informed Neural State Space Models via Learning and Evolution.

Elliott Skomski, Ján Drgona, Aaron Tuor

CoRR, 2020

Spectral Analysis and Stability of Deep Neural Dynamics.

Ján Drgona, Elliott Skomski, Soumya Vasisht, Aaron Tuor, Draguna L. Vrabie

CoRR, 2020

Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics.

Ján Drgona, Aaron Randall Tuor, Vikas Chandan, Draguna L. Vrabie

CoRR, 2020

Differentiable Predictive Control: An MPC Alternative for Unknown Nonlinear Systems using Constrained Deep Learning.

Ján Drgona, Karol Kis, Aaron Tuor, Draguna L. Vrabie, Martin Klauco

CoRR, 2020

Constrained Physics-Informed Deep Learning for Stable System Identification and Control of Unknown Linear Systems.

Ján Drgona, Aaron Tuor, Draguna L. Vrabie

CoRR, 2020

Constrained Neural Ordinary Differential Equations with Stability Guarantees.

Aaron Tuor, Ján Drgona, Draguna L. Vrabie

CoRR, 2020

All you need to know about model predictive control for buildings.

Ján Drgona, Javier Arroyo, Iago Cupeiro Figueroa, David H. Blum, Krzysztof Arendt, Donghun Kim, Enric Perarnau Ollé, Juraj Oravec, Michael Wetter, Draguna L. Vrabie, Lieve Helsen

Annu. Rev. Control., 2020

2018

MPC-based reference governor control of a continuous stirred-tank reactor.

Juraj Holaza, Martin Klauco, Ján Drgona, Juraj Oravec, Michal Kvasnica, Miroslav Fikar

Comput. Chem. Eng., 2018

2017

Optimal control of a laboratory binary distillation column via regionless explicit MPC.

Ján Drgona, Martin Klauco, Filip Janecek, Michal Kvasnica

Comput. Chem. Eng., 2017

2016

Regionless explicit MPC of a distillation column.

Ján Drgona, Filip Janecek, Martin Klauco, Michal Kvasnica

Proceedings of the 15th European Control Conference, 2016

2015

MPC-based reference governors for thermostatically controlled residential buildings.

Ján Drgona, Martin Klauco, Michal Kvasnica

Proceedings of the 54th IEEE Conference on Decision and Control, 2015

2013

Explicit MPC of LPV systems in the controllable canonical form.

Michal Kvasnica, Alexander Szucs, Miroslav Fikar, Ján Drgona

Proceedings of the 12th European Control Conference, 2013

Explicit stochastic MPC approach to building temperature control.

Ján Drgona, Michal Kvasnica, Martin Klauco, Miroslav Fikar

Proceedings of the 52nd IEEE Conference on Decision and Control, 2013