Heat Equation Solver

This project uses Machine Learning to solve 2nd order partial differential equations (PDEs) in Chemical Engineering. PDEs are fundamental to modeling and analyzing various phenomena in Chemical Engineering. Machine Learning offers a promising solution to solve these complex equations. The project develops a predictive model to approximate the solution to PDEs. Different Machine Learning algorithms are investigated, including Neural Networks and Gaussian Processes. The performance of the Machine Learning model is compared to traditional numerical methods. The project demonstrates the potential applications of Machine Learning in Chemical Engineering. These applications include process optimization, design of experiments, and control systems. The project has the potential to transform the field of Chemical Engineering. It provides a fast, accurate, and efficient method for solving complex PDEs.

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