Optimization using data from complex simulations has become an attractive decision-
making option, due to ability to embed high-fidelity, non-linear understanding of processes …

This paper presents new methods for set-valued state estimation of discrete-time nonlinear
systems whose trajectories are known to satisfy nonlinear equality constraints, called …

In this paper, we propose a computationally efficient framework for interval reachability of
systems with neural network controllers. Our approach leverages inclusion functions for the …

Automated fault detection (FD) methods are essential for safe and profitable operation of
complex engineered systems. Both data-driven and model-based methods have been …

This paper proposes a tractable family of remainder-form mixed-monotone decomposition
functions that are useful for over-approximating the image set of nonlinear mappings in …

We present a deterministic global optimization method for nonlinear programming
formulations constrained by stiff systems of ordinary differential equation (ODE) initial value …

In this article, we propose a tractable family of remainder-form mixed-monotone
decomposition functions that are useful for overapproximating the image set of nonlinear …

Automated fault diagnosis algorithms aim to identify the root cause of a fault after it is
detected, which is crucial for determining a safe and effective response. Set-based methods …

This article presents new methods for computing tight interval enclosures of the reachable
sets of discrete-time nonlinear systems subject to bounded uncertainties. These methods …

McCormick's relaxation technique is one of the most versatile and commonly used methods
for computing the convex relaxations necessary for deterministic global optimization. The …