The field of dynamical systems is being transformed by the mathematical tools and
algorithms emerging from modern computing and data science. First-principles derivations …

Mathematical models of biochemical reaction networks are important tools in systems
biology and systems medicine, eg, to analyze disease causes or to make predictions for the …

We present a flexible data-driven method for dynamical system analysis that does not
require explicit model discovery. The method is rooted in well-established techniques for …

To verify the correct operation of systems, engineers need to determine the set of
configurations of a dynamical model that are able to safely reach a specified configuration …

Excerpt This book grew out of multiple stimulating conversations with Nathan Kutz while I
was a postdoc at the University of Washington. It began with joking about taking our favorite …

Mathematical models of biochemical reaction networks are important tools in systems
biology and systems medicine to understand the reasons for diseases like cancer, and to …

Probabilistic and set-based methods are two approaches for model (in) validation,
parameter and state estimation. Both classes of methods use different types of data, ie …

Mathematical modeling has become an indispensable tool in the analysis, prediction and
control of chemical and biological systems. However, the estimation of consistent model …

We use Lyapunov-like functions and convex optimization to propagate uncertainty in the
initial condition of nonlinear systems governed by ordinary differential equations. We …

The understanding of biological systems and processes requires the development of
dynamical models characterized by nonlinear laws and often intricate regulation …