Ján Drgona

Publication Activity (10 Years)

Years Active: 2021-2024
Publications (10 Years): 36

Top Topics

Predictive Control

Machine Learning

Differential Equations

Top Venues

Publications

Ján Drgona, Aaron Tuor, Draguna L. Vrabie
Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees. IEEE Trans. Syst. Man Cybern. Syst. 54 (6) (2024)
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)
Ethan King, James Kotary, Ferdinando Fioretto, Ján Drgona
Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming. CoRR (2024)
James Koch, Madelyn Shapiro, Himanshu Sharma, Draguna L. Vrabie, Ján Drgona
Neural Differential Algebraic Equations. CoRR (2024)
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)
Renukanandan Tumu, Wenceslao Shaw-Cortez, Ján Drgona, Draguna L. Vrabie, Sonja Glavaski
Differentiable Predictive Control for Large-Scale Urban Road Networks. CoRR (2024)
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. ACC (2023)
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 (2023)
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)
Shimiao Li, Ján Drgona, Shrirang Abhyankar, Larry T. Pileggi
Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning. CoRR (2023)
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. ACM Comput. Surv. 55 (11) (2023)
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 (2023)
Shimiao Li, Ján Drgona, Shrirang Abhyankar, Larry T. Pileggi
Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning. e-Energy (Companion) (2023)
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)
Wenceslao Shaw-Cortez, Ján Drgona, Draguna L. Vrabie, Mahantesh Halappanavar
Robust Differentiable Predictive Control with Safety Guarantees: A Predictive Safety Filter Approach. CoRR (2023)
N. S. Karthik Somayaji, Yu Wang, Malachi Schram, Ján Drgona, Mahantesh Halappanavar, Frank Liu, Peng Li
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory. CoRR (2023)
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 (2022)
Ján Drgona, Sayak Mukherjee, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie
Learning Stochastic Parametric Differentiable Predictive Control Policies. CoRR (2022)
Ethan King, Ján Drgona, Aaron Tuor, Shrirang Abhyankar, Craig Bakker, Arnab Bhattacharya, Draguna L. Vrabie
Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem. CoRR (2022)
James Koch, Zhao Chen, Aaron Tuor, Ján Drgona, Draguna L. Vrabie
Structural Inference of Networked Dynamical Systems with Universal Differential Equations. CoRR (2022)
Sayak Mukherjee, Ján Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie
Neural Lyapunov Differentiable Predictive Control. CDC (2022)
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)
Shrirang Abhyankar, Ján Drgona, Andrew August, Elliott Skomski, Aaron Tuor
Neuro-physical dynamic load modeling using differentiable parametric optimization. CoRR (2022)
Ethan King, Ján Drgona, Aaron Tuor, Shrirang Abhyankar, Craig Bakker, Arnab Bhattacharya, Draguna L. Vrabie
Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem. ACC (2022)
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 (2022)
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)
Wenceslao Shaw-Cortez, Soumya Vasisht, Aaron Tuor, Ján Drgona, Draguna L. Vrabie
Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles. CoRR (2022)
Wenceslao Shaw-Cortez, Ján Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie
Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach. CoRR (2022)
Aowabin Rahman, Ján Drgona, Aaron Tuor, Jan Strube
Neural Ordinary Differential Equations for Nonlinear System Identification. ACC (2022)
Sayak Mukherjee, Ján Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie
Neural Lyapunov Differentiable Predictive Control. CoRR (2022)
Aowabin Rahman, Ján Drgona, Aaron Tuor, Jan Strube
Neural Ordinary Differential Equations for Nonlinear System Identification. CoRR (2022)
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 (2022)
Ján Drgona, Sayak Mukherjee, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar
On the Stochastic Stability of Deep Markov Models. CoRR (2021)
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 (2021)
Ján Drgona, Sayak Mukherjee, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar
On the Stochastic Stability of Deep Markov Models. NeurIPS (2021)
Kingsley Nweye, Zoltán Nagy, Sharada 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)