70 citations · 145 across the 28 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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…
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…