Reaction diffusion system prediction based on convolutional neural network.

Area of Science:

• Computational chemistry
• Chemical kinetics
• Pattern formation

Background:

• Reaction-diffusion systems model chemical reactions and spatial patterns.
• Traditional numerical methods like the finite element method (FEM) are computationally intensive for complex systems.

Purpose of the Study:

• To develop a machine learning model for rapid and accurate prediction of concentration distributions in a 2D one-component reaction-diffusion system.
• To bypass the computational cost of traditional numerical methods.

Main Methods:

• An encoder-decoder based convolutional neural network (CNN) was designed and trained.
• The CNN model takes simulation parameters, boundary conditions, geometry, and time as input features.
• The model learns time-dependent behavior directly from the time input feature.

Main Results:

• The CNN model achieved high prediction accuracy (mean relative error <3.04%).
• The machine learning approach is approximately 300 times faster than the traditional FEM.
• The model successfully predicts concentration distributions at specific time points.

Conclusions:

• A CNN-based model offers a fast and accurate alternative for simulating reaction-diffusion systems.
• This approach can significantly reduce computational resources for complex chemical reaction modeling.
• The developed model provides a powerful tool for analyzing spatio-temporal patterns in chemical processes.