Ján Drgoňa

3400 N Charles St,

Baltimore, MD 21218

I am an associate professor in the Department of Civil and Systems Engineering at Johns Hopkins University (JHU). I hold a secondary appointment at the Department of Electrical and Computer Engineering, and I am the core faculty member of the Ralph S. O’Connor Sustainable Energy Institute (ROSEI), and an affiliate of the Data Science and AI Institute (DSAI).

Previously, I was a research data scientist at the Pacific Northwest National Laboratory (PNNL), and a postdoc at KU Leuven in Belgium. I did my PhD in process control from Slovak University of Technology.

My project portfolio is focused on differentiable programming and scientific machine learning (SciML) for dynamical systems, constrained optimization, and control with applications to sustainable energy systems.

I am a lead software developer of Neuromancer SciML library in PyTorch for solving constrained optimization, physics-informed machine learning, and optimal control problems.

news

Nov 17, 2025	I gave an online invited talk at the MIT JTL Seminar organized by the Urban Mobility Lab (JTL).
Nov 17, 2025	I gave an online invited talk at the Boğaziçi University in Türkiye.
Nov 10, 2025	I gave an online invited talk at the University of Central Florida.
Oct 29, 2025	I gave a talk on Scientific Machine Learning for Optimization and Control at INFORMS in Atlanta, GA.
Oct 6, 2025	I gave a talk at the roundtable event on Construction and Environmental Aspects of Data Centers hosted by the University of Maryland’s Department of Civil and Environmental Engineering.

selected publications

Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees

Ján Drgoňa, Aaron Tuor, and Draguna Vrabie

IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024

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We present differentiable predictive control (DPC), a method for offline learning of constrained neural control policies for nonlinear dynamical systems with performance guarantees. We show that the sensitivities of the parametric optimal control problem can be used to obtain direct policy gradients. Specifically, we employ automatic differentiation (AD) to efficiently compute the sensitivities of the model predictive control (MPC) objective function and constraints penalties. To guarantee safety upon deployment, we derive probabilistic guarantees on closed-loop stability and constraint satisfaction based on indicator functions and Hoeffding’s inequality. We empirically demonstrate that the proposed method can learn neural control policies for various parametric optimal control tasks. In particular, we show that the proposed DPC method can stabilize systems with unstable dynamics, track time-varying references, and satisfy nonlinear state and input constraints. Our DPC method has practical time savings compared to alternative approaches for fast and memory-efficient controller design. Specifically, DPC does not depend on a supervisory controller as opposed to approximate MPC based on imitation learning. We demonstrate that, without losing performance, DPC is scalable with greatly reduced demands on memory and computation compared to implicit and explicit MPC while being more sample efficient than model-free reinforcement learning (RL) algorithms.
@article{Drgona2024_DPC, author = {Drgoňa, Ján and Tuor, Aaron and Vrabie, Draguna}, journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems}, title = {Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees}, year = {2024}, volume = {54}, number = {6}, pages = {3596-3607}, keywords = {Predictive control;Optimization;Computational modeling;Programming;Sensitivity;Optimal control;Closed loop systems;Differentiable programming;learning for control;parametric optimal control;policy optimization (PO)}, doi = {10.1109/TSMC.2024.3368026} }
Constructing Neural Network Based Models for Simulating Dynamical Systems

Christian Legaard, Thomas Schranz, Gerald Schweiger, and 6 more authors

ACM Computing Surveys, Feb 2023

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Dynamical systems see widespread use in natural sciences like physics, biology, and chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics, and control. For simple systems, the differential equations governing the dynamics can be derived by applying fundamental physical laws. However, for more complex systems, this approach becomes exceedingly difficult. Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. In recent years, there has been an increased interest in applying data-driven modeling techniques to solve a wide range of problems in physics and engineering. This article provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome. Based on the reviewed literature and identified challenges, we provide a discussion on promising research areas.
@article{Legaard2023, author = {Legaard, Christian and Schranz, Thomas and Schweiger, Gerald and Drgo\v{n}a, J\'{a}n and Falay, Basak and Gomes, Cl\'{a}udio and Iosifidis, Alexandros and Abkar, Mahdi and Larsen, Peter}, title = {Constructing Neural Network Based Models for Simulating Dynamical Systems}, year = {2023}, issue_date = {November 2023}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {55}, number = {11}, issn = {0360-0300}, url = {https://doi.org/10.1145/3567591}, doi = {10.1145/3567591}, journal = {ACM Computing Surveys}, month = feb, articleno = {236}, numpages = {34}, keywords = {physics-informed neural networks, physics-based regularization, Neural ODEs} }
Differentiable predictive control: Deep learning alternative to explicit model predictive control for unknown nonlinear systems

Ján Drgoňa, Karol Kiš, Aaron Tuor, and 2 more authors

Journal of Process Control, Aug 2022

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We present differentiable predictive control (DPC) as a deep learning-based alternative to the explicit model predictive control (MPC) for unknown nonlinear systems. In the DPC framework, a neural state-space model is learned from time-series measurements of the system dynamics. The neural control policy is then optimized via stochastic gradient descent approach by differentiating the MPC loss function through the closed-loop system dynamics model. The proposed DPC method learns model-based control policies with state and input constraints, while supporting time-varying references and constraints. In embedded implementation using a Raspberry-Pi platform, we experimentally demonstrate that it is possible to train constrained control policies purely based on the measurements of the unknown nonlinear system. We compare the control performance of the DPC method against explicit MPC and report efficiency gains in online computational demands, memory requirements, policy complexity, and construction time. In particular, we show that our method scales linearly compared to exponential scalability of the explicit MPC solved via multiparametric programming.
@article{DRGONA202280, title = {Differentiable predictive control: Deep learning alternative to explicit model predictive control for unknown nonlinear systems}, journal = {Journal of Process Control}, volume = {116}, pages = {80-92}, year = {2022}, month = aug, issn = {0959-1524}, doi = {https://doi.org/10.1016/j.jprocont.2022.06.001}, url = {https://www.sciencedirect.com/science/article/pii/S0959152422000981}, author = {Drgoňa, Ján and Kiš, Karol and Tuor, Aaron and Vrabie, Draguna and Klaučo, Martin}, keywords = {Differentiable predictive control, Model predictive control, Neural state space models, Data-driven differentiable optimization, Deep learning} }

On the Stochastic Stability of Deep Markov Models

Ján Drgoňa, Sayak Mukherjee, Jiaxin Zhang, and 2 more authors

In Advances in Neural Information Processing Systems, Aug 2021

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@inproceedings{NEURIPS2021_c9dd73f5,
  author = {Drgoňa, Ján and Mukherjee, Sayak and Zhang, Jiaxin and Liu, Frank and Halappanavar, Mahantesh},
  booktitle = {Advances in Neural Information Processing Systems},
  editor = {Ranzato, M. and Beygelzimer, A. and Dauphin, Y. and Liang, P.S. and Vaughan, J. Wortman},
  pages = {24033--24047},
  publisher = {Curran Associates, Inc.},
  title = {On the Stochastic Stability of Deep Markov Models},
  url = {https://proceedings.neurips.cc/paper/2021/hash/c9dd73f5cb96486f5e1e0680e841a550-Abstract.html},
  volume = {34},
  year = {2021}
}

All you need to know about model predictive control for buildings

Ján Drgoňa, Javier Arroyo, Iago Cupeiro Figueroa, and 8 more authors

Annual Reviews in Control, Sep 2020

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@article{DRGONA2020190,
  title = {All you need to know about model predictive control for buildings},
  journal = {Annual Reviews in Control},
  volume = {50},
  month = sep,
  pages = {190-232},
  year = {2020},
  issn = {1367-5788},
  doi = {https://doi.org/10.1016/j.arcontrol.2020.09.001},
  url = {https://www.sciencedirect.com/science/article/pii/S1367578820300584},
  author = {Drgoňa, Ján and Arroyo, Javier and {Cupeiro Figueroa}, Iago and Blum, David and Arendt, Krzysztof and Kim, Donghun and Ollé, Enric Perarnau and Oravec, Juraj and Wetter, Michael and Vrabie, Draguna L. and Helsen, Lieve},
  keywords = {Model predictive control, Building climate control, MPC formulation, MPC software tools, MPC implementation},
  abs = {It has been proven that advanced building control, like model predictive control (MPC), can notably reduce the energy use and mitigate greenhouse gas emissions. However, despite intensive research efforts, the practical applications are still in the early stages. There is a growing need for multidisciplinary education on advanced control methods in the built environment to be accessible for a broad range of researchers and practitioners with different engineering backgrounds. This paper provides a unified framework for model predictive building control technology with focus on the real-world applications. From a theoretical point of view, this paper presents an overview of MPC formulations for building control, modeling paradigms and model types, together with algorithms necessary for real-life implementation. The paper categorizes the most notable MPC problem classes, links them with corresponding solution techniques, and provides an overview of methods for mitigation of the uncertainties for increased performance and robustness of MPC. From a practical point of view, this paper delivers an elaborate classification of the most important modeling, co-simulation, optimal control design, and optimization techniques, tools, and solvers suitable to tackle the MPC problems in the context of building climate control. On top of this, the paper presents the essential components of a practical implementation of MPC such as different control architectures and nuances of communication infrastructures within supervisory control and data acquisition (SCADA) systems. The paper draws practical guidelines with a generic workflow for implementation of MPC in real buildings aimed for contemporary adopters of this technology. Finally, the importance of standardized performance assessment and methodology for comparison of different building control algorithms is discussed.}
}