With the rising application of systems biology, sensitivity analysis methods have been widely
applied to study the biological systems, including metabolic networks, signalling pathways …

Nonlinear model predictive control (NMPC) allows one to explicitly treat nonlinear dynamics
and constraints. To apply NMPC in real time on embedded hardware, online algorithms as …

The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …

Early prediction of cardiotoxicity is critical for drug development. Current animal models
raise ethical and translational questions, and have limited accuracy in clinical risk prediction …

Solving for detailed chemical kinetics remains one of the major bottlenecks for
computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry …

In recent years, the SUite of Nonlinear and DIfferential/ALgebraic equation Solvers
(SUNDIALS) has been redesigned to better enable the use of application-specific and third …

SUNDIALS is a suite of advanced computational codes for solving large-scale problems that
can be modeled as a system of nonlinear algebraic equations, or as initial-value problems in …

With the revived interest in hydrogen (H 2) as a direct combustion fuel for engine
applications, a computational study is conducted to assess the characteristics of H 2 direct …

Active safety systems are of significant importance for autonomous vehicles operating in
safety-critical situations like an obstacle-avoidance manoeuvre with high vehicle speed or …

An important problem in systems neuroscience is to identify the latent dynamics underlying
neural population activity. Here we address this problem by introducing a low-dimensional …