1 citations · 1 across the 3 of their papers we have counts for
3 papers
RTS Smoother-Guided Learning of Physics-Based Neural Differential Models
Ahmet Demirkaya, Georgios Stratis, Tales Imbiriba +2
Ordinary differential equations (ODEs) are widely used to model dynamical systems in physics, biology, neuroscience, and physiology, but in many applications some equations of the…
Learning Physics Informed Neural ODEs With Partial Measurements
Paul Ghanem, Ahmet Demirkaya, Tales Imbiriba +3
Learning dynamics governing physical and spatiotemporal processes is a challenging problem, especially in scenarios where states are partially measured. In this work, we tackle the…
Cubature Kalman Filter Based Training of Hybrid Differential Equation Recurrent Neural Network Physiological Dynamic Models
Ahmet Demirkaya, Tales Imbiriba, Kyle Lockwood +5
Modeling biological dynamical systems is challenging due to the interdependence of different system components, some of which are not fully understood. To fill existing gaps in our…