Deep neural networks have repeatedly been shown to be non-robust to the uncertainties of
the real world, even to naturally occurring ones. A vast majority of current approaches have …

Background: Considering current insulin action profiles and the nature of glycemic
responses to insulin, there is an acute need for longer term, accurate, blood glucose …

However, a major limitation of these works is the consideration of robustness as the only
impact noise has on trained ANNs. Additionally, even though the constant use of linear …

Recent advances in deep learning have bolstered our ability to forecast the evolution of
dynamical systems, but common neural networks do not adhere to physical laws, critical …

Neural networks present a useful framework for learning complex dynamics, and are
increasingly being considered as components to closed loop predictive control algorithms …

Deep neural networks have emerged as the workhorse for a large section of robotics and
control applications, especially as models for dynamical systems. Such data-driven models …

A major challenge in fitting models to glucose metabolism in people with type 1 diabetes is
incomplete data as its collection partially relies on self-reporting and does not include all …

Neural networks have emerged as one of the most powerful representation tools for a large
variety of modern decision making algorithms. Yet, there is a lack of rigorous mathematical …

In safety-critical robotics and medical applications, it is common to use deep neural networks
to capture the evolution of dynamical systems. This is particularly useful in modeling medical …

Artificial pancreas (AP) systems refer to a set of increasingly closed-loop biomedical devices
which seek to automate blood glucose regulation in individuals with type-1 diabetes mellitus …