A Neural Network Architecture for Obtaining Numerical Solutions of Integral Equation
- Resource Type
- Conference
- Authors
- Krithika, S; Singh, Soumyendra.; Prasanna Kumar, R
- Source
- 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) Electrical, Electronics and Computer Science (SCEECS), 2024 IEEE International Students' Conference on. :1-6 Feb, 2024
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Training
Technological innovation
Monte Carlo methods
Computational modeling
Integral equations
Neural networks
Computer architecture
Monte-Carlo method
Integral equation
Adam
Gated Recurrent Unit Model
- Language
- ISSN
- 2688-0288
This work proposes a neural network based approach using Gated Recurrent Unit neural network to solve integral equations. The proposed method employs Monte Carlo method to discretize the integral equations into a set of linear algebraic equations followed by a neural network based method to obtain numerical solutions. We demonstrate the versatility of our proposed systems using a numerical example of the second kind of Fredholm Integral equations. Adaptive moment estimation (ADAM) optimization has been used in the neural network to find the convergence. Clear illustrations with the use of loss function graphs, solution curve pertaining to actual and predicted function values are provided to help understand the behaviour of the solutions.