Course detail

Optimization Methods and Artificial Intelligence in Computational Simulations

FSI-LOMAcad. year: 2025/2026

Optimization is a key element in the process of finding the best solution to a given engineering problem. Students will be introduced to the theoretical background of optimization and artificial intelligence techniques and then learn how to apply these principles to specific computational simulations. The course includes classical mathematical programming methods, gradient methods, heuristic approaches, the study of evolutionary algorithms, and artificial intelligence methods with a focus on artificial neural networks, both classical layered topologies and convolutional neural networks and feedback models.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Tomáš Mauder, Ph.D.

Department

Energy Institute (EÚ)

Entry knowledge

Advanced knowledge of computational modeling and simulation techniques. Basic knowledge of mathematical analysis, linear algebra, probability, statistics, and numerical methods within the scope of engineering degree requirements.

Rules for evaluation and completion of the course

Students will work on the assigned semester project. The presentation of the project will be followed by a professional discussion and classification.

Aims

Deepening theoretical and practical knowledge and skills in the field of optimization and AI methods and their implementation for computer simulation methods.

Deepening and expanding knowledge of programming in Python.

Study aids

Prerequisites and corequisites

Not applicable.

Basic literature

Bazaraa,M. et al.: Nonlinear Programming: Theory and Algorithms. Wiley and Sons, 2006
Gabriel A. Hackebeil et al.: Pyomo - Optimization Modeling in Python. Springer Nature Switzerland, 2021
Klapka,J. a kol.: Metody operačního výzkumu. FSI, 2001
Paradalos et al.: Handbook of Applied Optimization. Oxford unioversity press, 2002
Xiao-Zhi Gao et al.: Applications of Artificial Intelligence in Engineering. Springer Singapore, 2021
Yang, Xin-She: Engineering optimization:An Introduction with Metaheuristic Applications. Wiley and Sons, 2010

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme N-SUE-P Master's 2 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Tomáš Mauder, Ph.D.

Syllabus

  1. Introduction to optimization, meaning of optimization, basic concepts
  2. Linear, non-linear, stochastic, integer programming
  3. Heuristic methods, gradient, evolutionary, swarm, human based methods
  4. Artificial intelligence - introduction, state space search
  5. Machine learning methods, basic tasks (regression problem, classification problem)
  6. Artificial neural networks - neuron, topology, activation functions
  7. Artificial neural networks - learning algorithm
  8. Convolutional neural networks
  9. Backpropagation neural networks

Computer-assisted exercise

26 hod., compulsory

Teacher / Lecturer

doc. Ing. Tomáš Mauder, Ph.D.

Syllabus

  1. Linear programming in Python (Matlab)
  2. Heuristic methods in Python (Matlab)
  3. Artificial Intelligence in Python (Matlab)
  4. Combining optimization algorithms in Python with computational simulations
  5. Machine learning methods using Matlab (deep learning toolbox)
  6. Machine learning methods using the Tensorflow framework (Python)