1 citations · 2 across the 5 of their papers we have counts for
6 papers
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…
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…
One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations
Henry Kvinge, Scott Howland, Nico Courts +9
The field of few-shot learning has made remarkable strides in developing powerful models that can operate in the small data regime. Nearly all of these methods assume every unlabel…
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…