70 citations · 76 across the 16 of their papers we have counts for
6 papers · 1 filter
ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs
Weimin Huang, Natalie M. Isenberg, Ján Drgoňa +2
Mixed Binary Quadratic Programs (MBQPs) are an important and complex set of problems in combinatorial optimization. As solving large-scale combinatorial optimization problems is ch…
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…
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…
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…
Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics
Jan Drgona, Aaron R. Tuor, Vikas Chandan +1
We present a physics-constrained control-oriented deep learning method for modeling building thermal dynamics. The proposed method is based on the systematic encoding of physics-ba…
Generative Adversarial Network based Autoencoder: Application to fault detection problem for closed loop dynamical systems
Indrasis Chakraborty, Rudrasis Chakraborty, Draguna Vrabie
Fault detection problem for closed loop uncertain dynamical systems, is investigated in this paper, using different deep learning based methods. Traditional classifier based method…