Models in Engineering
The first module begins with foundational concepts and an overview of the use of models in engineering. What is a model? What is the role of data and measurements? The second module will be devoted to mechanistic models: those that are built on prior physical principles. We will classify these into static and dynamical models. The third module will focus on data-based models, culminating with the modern techniques of deep learning. In the process we will learn the basic techniques of linear regression and logistic regression, as well as practical considerations such as training versus testing data sets and over-fitting.
Topics
- The main goal of the course is to provide foundational concepts and an overview of the use of models in engineering to answer questions such as: What is a model? What is the role of data and measurements? What are mechanistic, data-based, and mixed models? What are static and dynamical models? Where do optimization theory and control theory fit in?
- 1. A unified understanding of a number of modeling techniques, including regression, classification (deep neural networks), differential equations, and finite elements. 2. A grasp of the role of control theory and optimization in solving engineering problems. 3. A broad view of the available techniques, which will allow them to make better engineering decisions in their academic and professional careers.
Official catalogue course objectives and student learning outcomes
Prerequisites & course information
{'preRequisiteCourse': '1066961', 'concurrentPrerequisit': False}; {'preRequisiteCourse': '1147051', 'concurrentPrerequisit': False}
Sources
- Course catalogue ↗2026-2027