We introduce a new statistical verification algorithm that formally quantifies the behavioral
robustness of any time-continuous process formulated as a continuous-depth model. Our …

Abstract We show that Neural ODEs, an emerging class of time-continuous neural networks,
can be verified by solving a set of global-optimization problems. For this purpose, we …

Safe and optimal controller synthesis for switched-controlled hybrid systems, which combine
differential equations and discrete changes of the system's state, is known to be intricately …

Parameter identifiability is an important aspect of parameter estimation of dynamic system
modelling. Several methods exist to determine identifiability of parameter sets using the …

Cyber-physical systems tightly integrate computational resources with physical processes
through sensing and actuating, widely penetrating various safety-critical domains, such as …

This paper presents, in a unified fashion, deterministic as well as statistical Lagrangian-
verification techniques. They formally quantify the behavioral robustness of any time …

The main focus of this thesis is the development of a theory and associated algorithmsand
tools, for the deterministic and statistical reachability analysis, of cyber-physicalsystems …

Mathematical modelling of thyrotropic regulatory pathway provides important understanding
on the working of hypothalamus-pituitary-thyroid axis. An ordinary differential equation …

Stochastic parametric hybrid systems provide a means of formalising automata with
continuous nonlinear dynamics, discrete interruptions, and parametric uncertainty (eg …