Бакалавриат
2022/2023
Суррогатное моделирование и статистические методы в анализе инженерных систем
Статус:
Курс по выбору (Прикладная математика и информатика)
Направление:
01.03.02. Прикладная математика и информатика
Где читается:
Факультет компьютерных наук
Когда читается:
4-й курс, 1, 2 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Преподаватели:
Тараканов Александр Александрович
Язык:
английский
Кредиты:
5
Контактные часы:
60
Course Syllabus
Abstract
There is a variety of engineering systems (airplane shape design, oil and gas production and etc.) that can be accurately described by mathematical model or system of equations. Such equations can be solved numerically to address various practical challanges. Unforunately, numerical simulations of systems concerned can be time computationally expensive. However, surrogate modelling and machine learning methhods can be utilized to reduce computational cost of numerical simulations
In the present class, all steps from fundamentals (mathematical model) to design of optimal data acquisition system are explained. Therefore, folowing topics are covered in the present class:
- surrogate modelling techiques
- examples of engineering problems
- examples of application of surrogate models to engineering problems
The class can be useful for those who would like to broaden and/or deepen the knoledge in applications of Statistics and Machine Learning.
Learning Objectives
- To be able to solve some common engineering problems like Uncertainty Quantification and Design of Data Acquisition System starting from fundamentals aka mathematical model of a given engineering system.
Expected Learning Outcomes
- After the end of the class students should be able to solve some common engineering problems like Uncertainty Quantification and Design of Data Acquisition System starting from fundamentals aka mathematical model of a given engineering system. In other words, student will be able to solve engineering problems only with laptop that has only Numpy and no internet connection.
Course Contents
- Topic 1
- Topic 2
- Topic 3
- Topic 4
- Topic 5
- Topic 6
- Topic 7
- Topic 8
- Topic 9
- Topic 10
- Topic 11
- Topic 12
Assessment Elements
- Homework 1
- Homework 2Five problems 10 points each on topics covered on Lectures 4 – 10. Solutions should be presented as a report in Jupyter notebook: explanations and code that can be excuted so that results can be reproduced.
- Midterm Exam 1Six problems 10 points each on topics covered on Lectures 1 – 3. Problems do not require complex calculatios and may include proofs of some theorems
- Midterm Exam 2Six problems 10 points each on topics covered on Lectures 4 – 10. Problems do not require complex calculatios and may include proofs of some theorems
- Final ProjectIndividual project or project for a small group (depending on the total number of students). The objective is to perform Uncertaintty Quantificattion and/or Optimal Experimenta Design for different systems by utilizing methods from the entire class. Students are supposed to give a presentation of their results. The grade ranges from 0 to 10.
Interim Assessment
- 2022/2023 2nd module0.15 * Homework 1 + 0.15 * Homework 2 + 0.3 * Final Project + 0.2 * Midterm Exam 1 + 0.2 * Midterm Exam 2
Bibliography
Recommended Core Bibliography
- Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The Elements of Statistical Learning : Data Mining, Inference, and Prediction (Vol. Second edition, corrected 7th printing). New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=277008
Recommended Additional Bibliography
- Christopher M. Bishop. (n.d.). Australian National University Pattern Recognition and Machine Learning. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EBA0C705