Reaction-diffusion systems in natural sciences and new technology transfer

Abstract Diffusion mechanisms in natural sciences and innovation management involve partial differential equations (PDEs). This is due to their spatio-temporal dimensions. Functional semi-discretized PDEs (with lattice spatial structures or time delays) may be even more adapted to real world problems. In the modeling process, PDEs can also formalize behaviors, such as the logistic growth of populations with migration, and the adopters’ dynamics of new products in innovation models. In biology, these events are related to variations in the environment, population densities and overcrowding, migration and spreading of humans, animals, plants and other cells and organisms. In chemical reactions, molecules of different species interact locally and diffuse. In the management of new technologies, the diffusion processes of innovations in the marketplace (e.g., the mobile phone) are a major subject. These innovation diffusion models refer mainly to epidemic models. This contribution introduces that modeling process by using PDEs and reviews the essential features of the dynamics and control in biological, chemical and new technology transfer. This paper is essentially user-oriented with basic nonlinear evolution equations, delay PDEs, several analytical and numerical methods for solving, different solutions, and with the use of mathematical packages, notebooks and codes. The computations are carried out by using the software Wolfram Mathematica®7, and C++ codes.

[1]  Dimitri D. Vvedensky Partial differential equations - with Mathematica , 1993, Physics series.

[2]  Leah Edelstein-Keshet,et al.  Mathematical models in biology , 2005, Classics in applied mathematics.

[3]  A. Polyanin,et al.  Handbook of Nonlinear Partial Differential Equations , 2003 .

[4]  Robert A. Peterson,et al.  Models for innovation diffusion , 1985 .

[5]  Stephen Wolfram,et al.  The Mathematica Book , 1996 .

[6]  B. Øksendal Stochastic differential equations : an introduction with applications , 1987 .

[7]  V. B. Uvarov,et al.  Special Functions of Mathematical Physics: A Unified Introduction with Applications , 1988 .

[8]  明 大久保,et al.  Diffusion and ecological problems : mathematical models , 1980 .

[9]  L. Allen An introduction to stochastic processes with applications to biology , 2003 .

[10]  R. Triggiani,et al.  Control Theory for Partial Differential Equations: Continuous and Approximation Theories , 2000 .

[11]  S. Basov Partial differential equations in economics and finance , 2007 .

[12]  Yong Zhou,et al.  Qualitative analysis of delay partial difference equations , 2007 .

[13]  A. Polyanin Handbook of Linear Partial Differential Equations for Engineers and Scientists , 2001 .

[14]  John B. Herbich,et al.  Developments in Offshore Engineering: Wave Phenomena and Offshore Topics , 1998 .

[15]  N. Rashevsky,et al.  Mathematical biology , 1961, Connecticut medicine.

[16]  David W. K. Yeung,et al.  Cooperative Stochastic Differential Games , 2005 .

[17]  S. I. Rubinow,et al.  Introduction to Mathematical Biology , 1975 .

[18]  W. Wonham Random differential equations in control theory , 1970 .

[19]  N. Krylov Controlled Diffusion Processes , 1980 .

[20]  K. Åström Introduction to Stochastic Control Theory , 1970 .

[21]  J. Smoller Shock Waves and Reaction-Diffusion Equations , 1983 .

[22]  A. Cañada,et al.  Handbook of differential equations , 2004 .

[23]  L. Chambers Linear and Nonlinear Waves , 2000, The Mathematical Gazette.

[24]  P. Drazin,et al.  Solitons: An Introduction , 1989 .

[25]  N. Macdonald Time lags in biological models , 1978 .

[26]  Peter Gray,et al.  Chemical Oscillations and Instabilities: Non-Linear Chemical Kinetics , 1990 .

[27]  K. Morton,et al.  Numerical Solution of Partial Differential Equations , 1995 .

[28]  N. Britton Reaction-diffusion equations and their applications to biology. , 1989 .