70 citations · 145 across the 28 of their papers we have counts for
30 papers
Topological Feasibility Guarantees for Differentiable Predictive Control
Guangyu Wu, Ján Drgoňa
Differentiable predictive control (DPC), a self-supervised learning approach for approximating explicit model predictive control (MPC) policies, offers significant computational ad…
Homotopy-Guided Self-Supervised Learning of Parametric Solutions for AC Optimal Power Flow
Shimiao Li, Aaron Tuor, Draguna Vrabie +2
Learning to optimize (L2O) parametric approximations of AC optimal power flow (AC-OPF) solutions offers the potential for fast, reusable decision-making in real-time power system o…
Voltage-Regulated Sparse Optimization for Proactive Diagnosis of Voltage Collapses
Qinghua Ma, Seyyedali Hosseinalipour, Ming Shi +2
This paper aims to proactively diagnose and manage the voltage collapse risks, i.e., the risk of bus voltages violating the safe operational bounds, which can be caused by extreme…
Physics-informed machine learning for building performance simulation-A review of a nascent field
Zixin Jiang, Xuezheng Wang, Han Li +5
Building performance simulation (BPS) is critical for understanding building dynamics and behavior, analyzing performance of the built environment, optimizing energy efficiency, im…
Safe Physics-Informed Machine Learning for Dynamics and Control
Jan Drgona, Truong X. Nghiem, Thomas Beckers +6
This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models…
E-PINNs: Epistemic Physics-Informed Neural Networks
Bruno Jacob, Ashish S. Nair, Amanda A. Howard +2
Physics-informed neural networks (PINNs) have demonstrated promise as a framework for solving forward and inverse problems involving partial differential equations. Despite recent…
Efficient Primal Heuristics for Mixed Binary Quadratic Programs Using Suboptimal Rounding Guidance
Weimin Huang, Natalie M. Isenberg, Jan Drgona +2
Mixed Binary Quadratic Programs (MBQPs) are a class of NP-hard problems that arise in a wide range of applications, including finance, machine learning, and chemical and energy sys…
Differentiable Predictive Control for Robotics: A Data-Driven Predictive Safety Filter Approach
John Viljoen, Wenceslao Shaw Cortez, Jan Drgona +3
Model Predictive Control (MPC) is effective at generating safe control strategies in constrained scenarios, at the cost of computational complexity. This is especially the case in…
Learning Neural Differential Algebraic Equations via Operator Splitting
James Koch, Madelyn Shapiro, Himanshu Sharma +2
Differential algebraic equations (DAEs) describe the temporal evolution of systems that obey both differential and algebraic constraints. Of particular interest are systems that co…
A Robust, Efficient Predictive Safety Filter
Wenceslao Shaw Cortez, Jan Drgona, Draguna Vrabie +1
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. The…
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Truong X. Nghiem, Ján Drgoňa, Colin Jones +10
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathema…
Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning
Shimiao Li, Jan Drgona, Shrirang Abhyankar +1
Recent years have seen a rich literature of data-driven approaches designed for power grid applications. However, insufficient consideration of domain knowledge can impose a high r…
Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles
Wenceslao Shaw Cortez, Soumya Vasisht, Aaron Tuor +2
Conventional physics-based modeling is a time-consuming bottleneck in control design for complex nonlinear systems like autonomous underwater vehicles (AUVs). In contrast, purely d…
Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach
Wenceslao Shaw Cortez, Jan Drgona, Aaron Tuor +2
We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions. DPC is an unsupervised learning-based m…
Structural Inference of Networked Dynamical Systems with Universal Differential Equations
James Koch, Zhao Chen, Aaron Tuor +2
Networked dynamical systems are common throughout science in engineering; e.g., biological networks, reaction networks, power systems, and the like. For many such systems, nonlinea…
Neural Lyapunov Differentiable Predictive Control
Sayak Mukherjee, Ján Drgoňa, Aaron Tuor +2
We present a learning-based predictive control methodology using the differentiable programming framework with probabilistic Lyapunov-based stability guarantees. The neural Lyapuno…
Data-driven Stabilization of Discrete-time Control-affine Nonlinear Systems: A Koopman Operator Approach
Subhrajit Sinha, Sai Pushpak Nandanoori, Jan Drgona +1
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 bein…
Neuro-physical dynamic load modeling using differentiable parametric optimization
Shrirang Abhyankar, Jan Drgona, Andrew August +2
In this work, we investigate a data-driven approach for obtaining a reduced equivalent load model of distribution systems for electromechanical transient stability analysis. The pr…
Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem
Ethan King, Jan Drgona, Aaron Tuor +4
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 powe…
Neural Ordinary Differential Equations for Nonlinear System Identification
Aowabin Rahman, Ján Drgoňa, Aaron Tuor +1
Neural ordinary differential equations (NODE) have been recently proposed as a promising approach for nonlinear system identification tasks. In this work, we systematically compare…
Learning Stochastic Parametric Differentiable Predictive Control Policies
Ján Drgoňa, Sayak Mukherjee, Aaron Tuor +2
The problem of synthesizing stochastic explicit model predictive control policies is known to be quickly intractable even for systems of modest complexity when using classical cont…
Constructing Neural Network-Based Models for Simulating Dynamical Systems
Christian Møldrup Legaard, Thomas Schranz, Gerald Schweiger +6
Dynamical systems see widespread use in natural sciences like physics, biology, chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics…
Deep Learning Explicit Differentiable Predictive Control Laws for Buildings
Jan Drgona, Aaron Tuor, Soumya Vasisht +2
We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems. DPC poses an approximate solution to multipara…
Constrained Block Nonlinear Neural Dynamical Models
Elliott Skomski, Soumya Vasisht, Colby Wight +3
Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics. This work explores a novel formulation for…