Using Machine Learning to Reduce FEM Computational Power

Introduction

Using Machine Learning to Reduce FEM Computational Power

This course explores how machine learning, particularly linear regression with PyTorch, can reduce the computational power required for Finite Element Method (FEM) simulations. By training predictive models on pre-processed FEM datasets, participants will learn how to approximate results with high accuracy, significantly reducing computation time while maintaining acceptable error margins. Visualization of results and performance metrics such as R² score are also covered.

Learning Objectives

Using Machine Learning to Reduce FEM Computational Power

- Understand the role of FEM in engineering simulations.

- Learn how machine learning can approximate FEM outputs.

- Implement multi-input, multi-output regression using PyTorch.

- Train models with gradient descent optimization and evaluate loss functions.

- Interpret model accuracy using R² score and Mean Squared Error (MSE).

- Visualize predicted vs actual results using regression plots.