3 papers
cs.LG2026
Stability Enhanced Gaussian Process Variational Autoencoders
Carl R. Richardson, Jichen Zhang, Ethan King +1
A novel stability-enhanced Gaussian process variational autoencoder (SEGP-VAE) is proposed for indirectly training a low-dimensional linear time invariant (LTI) system, using high-…
eess.SY2026
Data-driven discovery and control of multistable nonlinear systems and hysteresis via structured Neural ODEs
Ike Griss Salas, Ethan King
Many engineered physical processes exhibit nonlinear but asymptotically stable dynamics that converge to a finite set of equilibria determined by control inputs. Identifying such s…
eess.SY2025
First Contact: Data-driven Friction-Stir Process Control
James Koch, Ethan King, WoongJo Choi +4
This study validates the use of Neural Lumped Parameter Differential Equations for open-loop setpoint control of the plunge sequence in Friction Stir Processing (FSP). The approach…