Chemical reaction networks (CRNs), defined by sets of species and possible reactions
between them, are widely used to interrogate chemical systems. To capture increasingly …

Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …

In this work, we review the state of the art on approaches for the determination of reaction
networks and the real-time monitoring of reactions in complex chemical systems consisting …

Optimal and safety-critical control are fundamental problems for stochastic systems, and are
widely considered in real-world scenarios such as robotic manipulation and autonomous …

We present a new framework towards the objective of learning coarse-grained models
based on the maximum entropy principle. We show that existing methods for assigning …

Data-based discovery of effective, coarse-grained (CG) models of high-dimensional
dynamical systems presents a unique challenge in computational physics and particularly in …

In this paper, we discuss the problem of model order reduction for positive real systems
based on balancing methods. The mixed gramian balanced truncation (MGBT) method …

The stochasticity of molecular motion results in the existence of multiple kinetically relevant
pathways in many biomolecular mechanisms. Because it is highly demanding to …

Reducing the order of positive real and bounded real systems has received great interest
over the years. Several methods have been published regarding these issues which are …

Two different approaches to parameter estimation (PE) in the context of polymerization are
introduced, refined, combined, and applied. The first is classical PE where one is interested …