Description

This course introduces students to the basics of artificial intelligence (AI) and its applications in mechanics. Topics covered include:

• Fundamentals of AI:
  • Linear Regression
  • Logistic Regression
  • Decision Tree Learning
  • Support Vector Machine (SVM)
  • k-Nearest Neighbors (k-NN)
  • Vanilla Gradient Descent
• Neural Networks and Deep Learning:
  • Artificial Neural Networks & Deep Learning
  • Recurrent Neural Networks (RNN) & Long Short-Term Memory (LSTM) methodology

Application
Students will apply these AI methods to implement and train models  in mechanical engineering fields, exploring how AI can enhance problem-solving in mechanics and optimization.

Compétences visées

By the end of the course, students will be able to:

• Understand and implement fundamental AI algorithms in mechanical applications.
• Develop neural network models and apply deep learning techniques.
• Train AI models to solve engineering problems in the mechanical domain.

Bibliographie

In fluid dynamics: Machine Learning for Fluid Mechanics, Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning,  Machine Learning Control,  Steven L. Brunton et al.

Contacts

Responsable(s) de l'enseignement

• Abderahmane Marouf