Computational Methods in Engineering

Code:

FIS3022

Acronym:

FIS3022

Keywords
Classification	Keyword
OFICIAL	Physics

Instance: 2024/2025 - 2S

Active?	Yes
Web Page:	http://moodle.up.pt/course/view.php?id=915
Responsible unit:	Department of Physics and Astronomy
Course/CS Responsible:	Bachelor in Engineering Physics

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L:EF	21	study plan from 2021/22	3	-	6	48	162

Teaching language

Suitable for English-speaking students

Objectives

• Learn methods and algorithms used in numerical simulation in physics/physical engineering.


• Analyze a set of problems in different areas of physics/physical engineering in view of their numerical solution.


• Build models fo the  problems.


• Describe and apply some basic numerical techniques.


• Contact with simulation methods.

Learning outcomes and competences

• Be able to structure a physics/physical engineering problem so that it can be solved by computational methods.


• Be able to select and adjust numerical methods for solving physics/physical engineering problems. 


• Being able to program a numerical solution of a problem, making use of implementations of numerical methods.


• Be able to analyze and criticize the results.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Domain of a programming language (intermediate level), with emphasis in Python.

Program

1. Basic concepts of computational modeling and numerical analysis.


2. Integral Transforms: Fourier  and Chebychev transforms.


3. Methods for solving partial differential equations: finite differences and spectral methods.


4. Special Topics. The specific contents of this section might vary from year to year. Examples of possible topics: nonlinear equations (traffic equation, nonlinear Schrödinger equation), wavelet transforms.

Mandatory literature

S. P. Venkateshan & Prasanna Swaminathan; Computational Methods in Engineering, Elsevier, 2013. ISBN: 9780124167032
Randall J. LeVeque; Finite difference methods for ordinary and partial differential equations. ISBN: 978-0-898716-29-0
Hans Petter Langtangen  Svein Linge; Finite Difference Computing with PDEs, Springer Open, 2017. ISBN: 978-3-319-55455-6

Complementary Bibliography

A First Course in Computational Physics, Paul L. De Vries, John Wiley and Sons, 1994
Numerical Techniques in Electromagnetics, M. Sadiku, CRC Press, 1991

Teaching methods and learning activities

Theoretical classes. Practical lessons in which students solve problems applying the methods learned.

Software

Scipy, matplotlib, sympy, jupyter notebook
distribuição Python, sendo imprescindíveis os pacotes numpy/scipy, matplotlib, sympy,notebook

keywords

Physical sciences > Physics > Computational physics
Physical sciences > Mathematics > Applied mathematics > Numerical analysis
Physical sciences > Computer science > Modelling tools

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	62,50
Teste	37,50
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	110,00
Frequência das aulas	52,00
Total:	162,00

Eligibility for exams

For a course with distributed evaluation, apply the following procedure to obtain frequency in which each student must: 1. Do not miss more than 1 / 4 of the lab classes given; 2. Deliver elements of evaluation in due time with a score above 30 in 100.


Since this is a course with distributed evaluation, the "recurso" exam refers allows only the componernt "final exam", with the rspective weight.

Calculation formula of final grade

The final grade will be calculated to the obtained by the following formula:
37.5% *RP + 62.5%*E
where RP= 3 tests or homework problems; E= final exam.

(see also comments section below)

Examinations or Special Assignments

Special assessment (TE, DA, ...)

Students with special arrangements which can not be assessed in accordance with the general scheme described above, should report the situation to the teacher during the first week of classes or even a week after the facts which substantiate the request for exception. The evaluation will be done in these situations in a case by case basis according to the criteria of the teacher.

Classification improvement

The improvement of the grade is made according to the following scheme:
1) Since this is a course with distributed evaluation,  improvment of the final grade only refers to the component "final exam", with the associated weight.
2) The jury of the course also reserves the right to call students to conduct an oral examination whenever it deems appropriate or to evaluate situations not considered in this regime.

Observations

UC's Jury : Augusto Silveira Rodrigues
João Viana Lopes

It is assumed that students know how to program in Python, and obtained aprovall in a previous UC in Computational Physics or equivalent.
Situations not covered in this regulation must be communicated to the teacher during the first week of classes or even a week after the facts which substantiate the request for exception. The solution of these situations will be made in a case by case basis according to the criteria of the teacher.