Machine Learning for Material and Structural Mechanics

Artificial neural networks (ANN) have gained significant popularity in recent years for many applications in engineering science. Of particular interest are applications related to material and structural mechanics. These include, among others, solving partial differential equations PDEs, material modeling, structural optimization, pattern recognition and real-time simulation.

This course presents an introduction to machine learning for engineering students. After successful completion of the module the students are able to:

• Use Machine Learning for the solution of PDEs
• Write their own Machine Learning code
• Predict material and structural properties using physics-informed Deep Neural Networks
• Employ geometric learning via Convolutional Neural Networks for computational mechanics

• Artificial neural networks (ANN) applications in mechanics
• Supervised/unsupervised ANN approaches: RNN, FFNN, CNN, PINN
• Simplified structural and material modeling (Basic, fundamental level)
• Computer lap using Tensorflow program