14 citations · 49 across the 17 of their papers we have counts for
5 papers · 1 filter
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…