14 citations · 49 across the 17 of their papers we have counts for
18 papers
Data Center Chiller Plant Optimization via Mixed-Integer Nonlinear Differentiable Predictive Control
Ján Boldocký, Cary Faulkner, Elad Michael +3
We present a computationally tractable framework for real-time predictive control of multi-chiller plants that involve both discrete and continuous control decisions coupled throug…
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…
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…
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…
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…
Prototypical Region Proposal Networks for Few-Shot Localization and Classification
Elliott Skomski, Aaron Tuor, Andrew Avila +5
Recently proposed few-shot image classification methods have generally focused on use cases where the objects to be classified are the central subject of images. Despite success on…
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…
Physics-Informed Neural State Space Models via Learning and Evolution
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, physi…
Dissipative Deep Neural Dynamical Systems
Jan Drgona, Soumya Vasisht, Aaron Tuor +1
In this paper, we provide sufficient conditions for dissipativity and local asymptotic stability of discrete-time dynamical systems parametrized by deep neural networks. We leverag…
Deep Learning Alternative to Explicit Model Predictive Control for Unknown Nonlinear Systems
Jan Drgona, Karol Kis, Aaron Tuor +2
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 fra…
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning
Henry Kvinge, Zachary New, Nico Courts +6
Deep learning has shown great success in settings with massive amounts of data but has struggled when data is limited. Few-shot learning algorithms, which seek to address this limi…
Learning Constrained Adaptive Differentiable Predictive Control Policies With Guarantees
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. We emplo…