Numerical Theory and Applications of Nonlinear Evolution Equations
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Difference and Differential Equations".
Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 1708
Special Issue Editors
Interests: high accurate and fast algorithms for nonlinear evolution equations; stability and numerical simulation of delayed differential equations; efficient numerical methods for fractional differential equations
Interests: geochemistry & geophysics; mathematics
Special Issue Information
Dear Colleagues,
The study of nonlinear phenomena is concerned in the field of natural science and even social science.
Since many phenomena in nature are essentially nonlinear, nonlinear phenomena have aroused the interest and concern of engineers, physicists, mathematicians, and many others. In the mathematical and physical sciences, nonlinearity is the phenomenon that the change in output is not proportional to that in input. A large part of nonlinear phenomena can be described by nonlinear partial differential equations.
It is often possible to find several particular solutions for nonlinear problems; however, it is commonly very difficult to find general solutions from these particular solutions. Hence, it is necessary to study the numerical theory and numerical simulation of the nonlinear evaluation equation.
This Special Issue addresses the newest development for the nonlinear evolution problems involving shallow water problems, nonlinear phase field equation, nonlinear modeling turbulence, nonlinear quantum mechanics, nonlinear bioinformation, nonlinear delay differential equations, nonlinear fractional problems with their possible applications in any area of science and engineering.
Prof. Dr. Qifeng Zhang
Prof. Dr. Kejia Pan
Prof. Dr. Zhong Li
Guest Editors
Manuscript Submission Information
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Keywords
- nonlinear evolution equation
- numerical stability
- convergence
- nonlinear phenomenon
- numerical simulation
- finite difference methods
- finite element methods
- finite volume methods
- spectral methods
- deep learning
