Neural network method for fractional-order partial differential equations

2020 ◽  
Vol 414 ◽  
pp. 225-237 ◽  
Author(s):  
Haidong Qu ◽  
Xuan Liu ◽  
Zihang She
Author(s):  
Nkosingiphile Mnguni ◽  
Sameerah Jamal

Abstract This paper considers two categories of fractional-order population growth models, where a time component is defined by Riemann–Liouville derivatives. These models are studied under the Lie symmetry approach, and we reduce the fractional partial differential equations to nonlinear ordinary differential equations. Subsequently, solutions of the latter are determined numerically or with the aid of Laplace transforms. Graphical representations for integral and trigonometric solutions are presented. A key feature of these models is the connection between spatial patterning of organisms versus competitive coexistence.


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