Wavelets based physics informed neural networks to solve non-linear differential equations
Ziya Uddin, Sai Ganga, Rishi Asthana, Wubshet Ibrahim
Abstract
In this study, the applicability of physics informed neural networks using wavelets as an activation function is discussed to solve non-linear differential equations. One of the prominent equations arising in fluid dynamics namely Blasius viscous flow problem is solved. A linear coupled differential equation, a non-linear coupled differential equation, and partial differential equations are also solved in order to demonstrate the method's versatility. As the neural network's optimum design is important and is problem-specific, the influence of some of the key factors on the model's accuracy is also investigated. To confirm the approach's efficacy, the outcomes of the suggested method were compared with those of the existing approaches. The suggested method was observed to be both efficient and accurate.