Applied Math for Engineers (EN.560.601) -- Level: Graduate, Format: Lecture
Sping: 2022, 2023
This course presents a broad survey of the basic mathematical methods used
in the solution of ordinary and partial differential equations: linear algebra, power series,
Fourier series, separation of variables and integral transforms.
Case Coding (EN.560.291) -- Level: Undergraduate, Format: Lecture
Fall: 2022
Having taken a computing course in the freshman year, this course allows students to further develop their programming skills to solve, understand, or automate problems specific to Civil and Systems Engineering. Students learn fundamental engineering and data analytics operations skills in a variety of softwares (e.g., R, Matlab, ArcGIS).
This course introduces fundamental programming concepts and techniques
using Python programming language. Topics covered include the design and implementation
of algorithms using variables, control structures, arrays, functions, files, testing, debugging,
and structured program design. Elements of object-oriented programming, algorithmic
efficiency and data visualization are also discussed.
This course introduces fundamental programming concepts and techniques
using MATLAB. Topics covered include the design and implementation of algorithms using
variables, control structures, arrays, functions, files, testing, debugging, and structured
program design. Elements of object-oriented programming, algorithmic efficiency and data
visualization are also discussed.
School of Pedagogical and Technological Education (ASPETE), Athens, Greece
Optimum Antiseismic Design of Structures -- Level: Graduate, Format: Lecture
Sping: 2016
Thic course covered topics of size, shape and topology optimization of
structures, using genetic and evolutionary optimization algorithms.
Artificial Neural Networks and Metaheuristic Algorithms in Structural Mechanics -- Level: Graduate, Format: Lecture
Sping: 2016
This course introduces the fundamentals of artificial neural networks; their architectures, training algorithms (supervised, unsupervised) and their use in the framework of structural mechanics. Moreover, an introduction to metaheuristic algorithms used to solve optimization problems is made, along with ways to use artificial neural networks for this purpose.
Teaching Assistant
National Technical University of Athens
Stochastic Finite Elements -- Level: Graduate, Format: Lecture
Spring: 2010-2015
This course introduces the fundamentals of the stochastic process theory and its applications. Topics covered include widely used methods for the simulation of stochastic processes, namely the Karhunen-Loeve expansion and spectral representation method. Moreover, the fundamental principles of the Stochastic Finite Element Method (SFEM) were discussed and the topic of reliability analysis methods was addressed.
