Computational Modelling and Simulation in Engineering Physics

General characterization

Prerequisites

Programming proficiency. Access to a computer. Undergraduate course in Physical Engineering or equivalent. 

Bibliography

“An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo” U. Wilensky, W. Rand (2015)

“Introduction to Statistical Physics” J. Casquilho, P. Teixeira (2015) QC174.8.CAS

“An Introduction to Computer Simulation Methods: applications to physical systems” H. Gould, J. Tobochnik, W. Christian (2006) QC52.GOU

“Genetic Algorithm Model Fitting”, M. Lybanon, K. Messa, in “Practical Handbook of Genetic Algorithms: Complex Coding Systems, Volume III”, L. Chambers Ed. (1998)

“A Guide to Simulation” P. Bratley, B. Fox, L. Schrag (1987) CIUL-1023

“Introduction to Computational Science: Modeling and Simulation for the Sciences”, A. B. Shiflet, G. W. Shiflet (2014)

“Basic Concepts in Computational Physics” B. A. Stickler, E. Schachinger (2016)

Teaching method

In each block of the program syllabus an introduction to the topic, and or methods, is presented by the teacher. The students implement a minimal base program, obtain results and analyze them. The program is changed by the students, with teacher supervision, other assumptions or methods of simulation are explored and the new results are analyzed and  compared with previous ones.  The students present the program to the teacher in the classroom and at a predefined date, the students deliver the final program.

Evaluation method

In accordance with the KNOWLEDGE ASSESSMENT RULES OF THE FACULTY OF SCIENCES AND TECHNOLOGY OF THE UNIVERSITY NOVA DE LISBOA (approved on January 16, 2018), this is a curricular unit with "Laboratory or Project Assessment". Carried out based on carrying out practical laboratory work, design or problem solving, and their reports and/or respective tests, carried out individually or in groups, and their discussion, if any;

1) Assessment by 2 computational works among those proposed by the teacher and carried out in a group with a grade of up to 9 points.

2) Summative assessment of performance and participation in classes and discussion on the various topics throughout the semester

Frequência at the UC by attending at least 2/3 of the classes.

Subject matter

• Brief Programming Review (R, Python, Java, and C++)

• Introduction to Numerical and Graphical Libraries:

• Numerical computation

• Data analysis, Modeling, Data visualization

• Dynamic Reports Using Markdown/Quarto

*Improve-Performance Computing with DLLs: C++/C# integration with R, Python, and VBA.

*Data Handling with SQL Integration

Core Topics

Block 1: Numerical Methods and Mathematical Modeling

Numerical Integration Methods

Random Variables and Discrete Distributions

Monte Carlo and Quasi-Monte Carlo Methods

Variance Reduction Methods

Finite Difference Method (FDM)

Diffusion Equation

Block 2: Statistical Analysis and Dimensionality Reduction

Introduction to Linear Regression

Principal Component Analysis (PCA)

Linear Discriminant Analysis (LDA)

Kernel Principal Component Analysis (kPCA)

Independent Component Analysis (ICA)

Support Vector Machines (SVM)

Block 3: Time Series Analysis and Forecasting

Introduction to Time Series Analysis

Time Series Models for Forecasting:

AR (Autoregressive Model)

MA (Moving Average Model)

ARMA (Autoregressive Moving Average Model)

ARIMA (Autoregressive Integrated Moving Average Model)

Advanced Volatility Models:

ARCH (Autoregressive Conditional Heteroskedasticity)

GARCH (Generalized Autoregressive Conditional Heteroskedasticity)

Programs

Programs where the course is taught: