no code implementations • 1 Apr 2024 • Ethan King, James Kotary, Ferdinando Fioretto, Jan Drgona
Recent work has shown a variety of ways in which machine learning can be used to accelerate the solution of constrained optimization problems.
no code implementations • 19 Mar 2024 • James Koch, Madelyn Shapiro, Himanshu Sharma, Draguna Vrabie, Jan Drgona
In this work, we show that the proposed NDAEs abstraction is suitable for relevant system-theoretic data-driven modeling tasks.
no code implementations • 14 Nov 2023 • Wenceslao Shaw Cortez, Jan Drgona, Draguna Vrabie, Mahantesh Halappanavar
In this paper, we propose a novel predictive safety filter that is robust to bounded perturbations and is implemented in an even-triggered fashion to reduce online computation.
no code implementations • 19 Oct 2023 • Yu Wang, Yuxuan Yin, Karthik Somayaji Nanjangud Suryanarayana, Jan Drgona, Malachi Schram, Mahantesh Halappanavar, Frank Liu, Peng Li
Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge.
no code implementations • 24 Aug 2023 • Karthik Somayaji NS, Yu Wang, Malachi Schram, Jan Drgona, Mahantesh Halappanavar, Frank Liu, Peng Li
Our work proposes to enhance the resilience of RL agents when faced with very rare and risky events by focusing on refining the predictions of the extreme values predicted by the state-action value function distribution.
no code implementations • 21 Apr 2023 • Shimiao Li, Jan Drgona, Shrirang Abhyankar, Larry Pileggi
Recent years have seen a rich literature of data-driven approaches designed for power grid applications.
no code implementations • 27 Nov 2022 • Hari Prasanna Das, Yu-Wen Lin, Utkarsha Agwan, Lucas Spangher, Alex Devonport, Yu Yang, Jan Drgona, Adrian Chong, Stefano Schiavon, Costas J. Spanos
In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient.
1 code implementation • 3 Aug 2022 • Wenceslao Shaw Cortez, Jan Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie
We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions.
no code implementations • 11 Jul 2022 • James Koch, Zhao Chen, Aaron Tuor, Jan Drgona, Draguna Vrabie
Networked dynamical systems are common throughout science in engineering; e. g., biological networks, reaction networks, power systems, and the like.
no code implementations • 26 Mar 2022 • Subhrajit Sinha, Sai Pushpak Nandanoori, Jan Drgona, Draguna Vrabie
In recent years data-driven analysis of dynamical systems has attracted a lot of attention and transfer operator techniques, namely, Perron-Frobenius and Koopman operators are being used almost ubiquitously.
no code implementations • 20 Mar 2022 • Shrirang Abhyankar, Jan Drgona, Andrew August, Elliot Skomski, Aaron Tuor
In this work, we investigate a data-driven approach for obtaining a reduced equivalent load model of distribution systems for electromechanical transient stability analysis.
1 code implementation • 16 Mar 2022 • Ethan King, Jan Drgona, Aaron Tuor, Shrirang Abhyankar, Craig Bakker, Arnab Bhattacharya, Draguna Vrabie
The dynamics-aware economic dispatch (DED) problem embeds low-level generator dynamics and operational constraints to enable near real-time scheduling of generation units in a power network.
no code implementations • 29 Sep 2021 • Yu Wang, Jan Drgona, Jiaxin Zhang, Karthik Somayaji NS, Frank Y Liu, Malachi Schram, Peng Li
Although various flow models based on different transformations have been proposed, there still lacks a quantitative analysis of performance-cost trade-offs between different flows as well as a systematic way of constructing the best flow architecture.
no code implementations • 25 Jul 2021 • Jan Drgona, Aaron Tuor, Soumya Vasisht, Elliott Skomski, Draguna Vrabie
We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems.
no code implementations • 6 Jan 2021 • Elliott Skomski, Soumya Vasisht, Colby Wight, Aaron Tuor, Jan Drgona, Draguna Vrabie
Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics.
no code implementations • 26 Nov 2020 • Elliott Skomski, Jan Drgona, Aaron Tuor
Recent works exploring deep learning application to dynamical systems modeling have demonstrated that embedding physical priors into neural networks can yield more effective, physically-realistic, and data-efficient models.
no code implementations • 26 Nov 2020 • Jan Drgona, Soumya Vasisht, Aaron Tuor, Draguna Vrabie
In this paper, we provide sufficient conditions for dissipativity and local asymptotic stability of discrete-time dynamical systems parametrized by deep neural networks.
no code implementations • 11 Nov 2020 • Jan Drgona, Aaron R. Tuor, Vikas Chandan, Draguna L. Vrabie
The proposed method is based on the systematic encoding of physics-based prior knowledge into a structured recurrent neural architecture.
no code implementations • 7 Nov 2020 • Jan Drgona, Karol Kis, Aaron Tuor, Draguna Vrabie, Martin Klauco
In the DPC framework, a neural state-space model is learned from time-series measurements of the system dynamics.
2 code implementations • 23 Apr 2020 • Jan Drgona, Aaron Tuor, Draguna Vrabie
We present differentiable predictive control (DPC), a method for learning constrained neural control policies for linear systems with probabilistic performance guarantees.
1 code implementation • ICLR Workshop DeepDiffEq 2019 • Aaron Tuor, Jan Drgona, Draguna Vrabie
Differential equations are frequently used in engineering domains, such as modeling and control of industrial systems, where safety and performance guarantees are of paramount importance.