This review outlines major directions of simpler model development in environmental
modeling, metamodeling, statistical-regression-and machine-learning-based empirical …

Numerical simulation is widely recognized as an efficient means of developing high-
performance combustors. However, its efficiency is limited by the growing size of chemical …

This paper reviews some mathematical models of biochemical reaction networks. The
models contain a large number species and reactions. This is a difficult task and it requires …

We propose a novel statistical learning framework for automatically and efficiently building
reduced kinetic Monte Carlo (KMC) models of large-scale elementary reaction networks …

Increases in both chemical kinetics knowledge and the capacity of computers have led to the
availability of very large detailed kinetic mechanisms for many problems. These …

Many complex kinetic models in the field of biochemical reactions contain a large number of
species and reactions. These models often require a huge array of computational tools to …

The century of complexity has come. The face of science has changed. Surprisingly, when
we start asking about the essence of these changes and then critically analyse the answers …

Large-scale nonlinear dynamical systems, such as models of atmospheric hydrodynamics,
chemical reaction networks, and electronic circuits, often involve thousands or more …

A complicated issue in many cell signaling pathways is mainly related to the number of state
variables and parameters. This is computationally very expensive, it may sometimes difficult …

We study a kinetic model of active export of importins. The kinetic model is written as a
system of ordinary differential equations. We developed some model reduction techniques …