Scientific Computing  program aims to produce highly skilled scientist capable of applying numerical methods and critical evaluation of their results to their field in science and engineering. It brings together best practice in computing with cutting edge science and fills in the computational gap in the traditional mathematical, science, and engineering programs. Therefore, the research areas in the program range from foundation mathematics and fundamental numerical algorithms to such practical topics in computational fluid dynamics, PDE-constrained optimization, model order reduction, statistical learning, computational biology, high performance computing, uncertainty quantification, etc.  So recommended elective courses can be categorized in the following specialized areas:

  • Computational Finance (also known as Financial Engineering) is a cross-disciplinary field; and comprises of subjects on mathematical finance, numerical methods, and computer simulations. Utilizing various methods of applied mathematics (and engineering), practitioners of computational finance aim to value financial instruments (financial derivatives, options, etc.), create optimal portfolios of such instruments, and determine risks of such portfolios (of financial instruments).
  • Computational Mathematics and Simulation comprises the rigorous mathematical analysis of numerical methods, often concerned with issues of discretization, approximation and convergence, the development and analysis of numerical algorithms, the implementation of these algorithms on modern computer architectures, and the use of numerical methods in conjunction with mathematical modeling to solve large-scale practical problems.  Particular research foci are numerical methods for partial differential equations (finite elements, adaptive finite element methods), numerical linear algebra, PDE-constrained optimization, model order reduction techniques, and algorithms for uncertainty quantification.
  • Data Science is a multidisciplinary field that uses statistics, mathematics, and computer science in order to extract significant information from various forms of data. Over the last decade it has become a very crucial topic for many industries: finance, insurance, healthcare, energy, web analytics, cybersecurity are some of them among many others.
  • Mathematical Modelling and Applications, a specialized area, is designed for students interested in the skills and knowledge required to develop efficient and robust numerical solutions to engineering  problems such as  heart electromechanics, flows in porous media, fluid dynamics, structural analysis, mass transport, heat transfer and, more in general, to multiscale and multiphysics applications. 
  • Operations Research (OR) is an analytical method of problem-solving and decision-making that is useful in the management of organizations. Analytical methods used in OR include mathematical logic, simulation, network analysis, queuing theory, and game theory. Some Techniques of Operations Research include: linear programming, transportation problem, assignment problem, queuing theory, game theory, inventory control models, and goal programming.

The list of possible elective courses is not limited to the list below; and the list is expanded on a continuing basis; courses not in the list can be accepted as elective subject to the approval of the student's adviser.

Possible Elective Courses @ METU
  • AE545      - Advanced Fluid Mechanics 
  • AE546      - Computational Fluid Dynamics on Unstructured Grids
  • AE714      - High Performance Computing in Aerospace Engineering
  • AE726      - Gas Turbine Heat Transfer and Cooling
  • BA6505    - Applied Regression Analysis
  • BA6506    - Applied Multivariate Analysis
  • BA6507    - Applied Time Series and Panel Data Analysis
  • CE4006    - Introduction to Computational Mechanics of Materials
  • CE7018    - Computational Inelasticity
  • CENG514  - Data Mining
  • CENG561  - Artificial Intelligence
  • CENG562  - Machine Learning
  • CENG574  - Statistical Data Analysis
  • CENG770  - Advanced Data Mining
  • CENG577  - Parallel Computing
  • CENG576  - Numerical Methods in Optimization
  • CENG593  - Deep Learning
  • CENG770  - Advanced Data Mining
  • CENG780  - Sparse Matrix Computations
  • CENG785  - Algorithmic Trading and Quantitative Strategies
  • CENG793  - Advanced Deep Learning
  • ECON507  - Econometric Methods I
  • ECON508  - Econometric Methods II
  • ECON645  - Applied Nonlinear Time Series Analysis
  • ECON680  - Time Series Econometrics
  • ECON685  - Topics in Time Series Econometrics
  • EE519       - Medical Imaging
  • EE522       - Numerical Methods for Electromagnetics
  • EE554       - Optimal Control Theory
  • EE574       - Power Sys. Real-Time Monitoring & Control
  • EE583       - Pattern Recognition
  • ESS507     - Climate Change and Modelling
  • GEOE537  - Flow through Porous Media
  • GEOE551  - Groundwater Modelling Techniques
  • IE460       - Introduction to Data Mining
  • IE4904     - Special Topics in IE: Multi-objective Combinatorial Optimization
  • IE4909     - Analysis and Optimization Methods in Finance
  • IE4910     - Game Theory and Economical Decision Analysis
  • IE4912     - Special Topics in IE: Stochastic Optimization with Applications
  • IE553       - Linear Optimization
  • IE554       - Discrete Optimization
  • IE555       - Nonlinear Optimization
  • IE558       - Multiobjective Decision Making
  • IE560       - Stochastic Programming
  • IE717       - Constraint Programming
  • IAM511    - Algorithms and Complexity
  • IAM520    - Financial Derivatives
  • IAM521    - Financial Management
  • IAM522    - Stochastic Calculus for Finance
  • IAM524    - Financial Economics
  • IAM526    - Time Series Applied to Finance
  • IAM528    - Markov Decision Processes
  • IAM529    - Applied Nonlinear Dynamics
  • IAM530    - Elements of Probability and Statistics
  • IAM542    - Stochastic Processes
  • IAM550    - Portfolio Optimization
  • IAM557    - Statistical Learning and Simulation
  • IAM567    - Mathematical Modelling
  • IAM612    - Financial Modelling with Jump Processes
  • IAM614    - Methods of Computational Finance
  • IAM615    - Advanced Stochastic Calculus for Finance
  • IAM664    - Inverse Problems
  • IAM745    - Special Topics: Stochastic and Deterministic Optimal Control with Applications to Finance
  • IAM749    - Numerical Algorithms with Financial Applications
  • IAM757    - Special Topics: Monte Carlo Methods in Finance and Insurance
  • IAM760    - Model Order Reduction
  • IAM762    - Adaptive Finite Elements and Optimal Control
  • IAM763    - Special Topics: Numerical Simulation in Fluid Dynamics
  • IAM766    - Special Topics: Optimal Control with Partial Differential Equations
  • IAM767    - Special Topics: Iterative Methods for Large Scale Linear and Nonlinear Equations
  • IAM768    - Special Topics: Method and Applications of Uncertainty Quantification
  • IAM769    - Special Topics: Reaction-Diffusion systems: Applications and Numerics
  • IAM770    - Special Topics: Discontinuous Galerkin Methods
  • IAM771    - Special Topics: Optimization Methods for Machine Learning
  • IS503       - Data Base Concepts and Applications
  • IS580       - Knowledge, Discovery and Mining
  • IS787       - Big Data
  • MASC503 - Introduction to Oceanography
  • MASC530 - Introduction to Physical  Oceanography
  • MASC547 - Modeling in Marine Environment
  • MATH570 - Functional Analysis
  • MATH583 - Partial Differential Equations
  • MATH595 - The Boundary Element Methods and Applications
  • MATH596 - Computational Basis of Fluid Dynamic Equations
  • ME507      - Applied Optimal Control
  • ME517      - Advanced Fluid Mechanics
  • ME547      - Introduction to Continuum Mechanics
  • ME581      - Finite Element Analysis in Solid Mechanics
  • MMI727    - Deep Learning : Methods and Applications
  • PETE556   - Analysis of Porous Media Flow Equations I
  • PETE557   - Analysis of Porous Media Flow Equations II
  • STAT525   - Regression Theory and Methods
  • STAT559   - Applied Multivariate Analysis
  • STAT560   - Logistic Regression Analysis
  • STAT561   - Panel Data Analysis
  • STAT562   - Univariate Time Series Analysis
  • STAT563   - Multivariate Time Series Analysis
  • STAT564   - Advanced Statistical Data Analysis
  • STAT612   - Advanced Topics in Time Series Analysis
  • STAT620   - Bayesian Inference
  • STAT728   - Joint Analysis of Mixed Discrete Continuous Data: Methods and Applications
  • STAT729   - Modern Data Analysis: From Hidden Markov Models to Statistical Learning

Last Updated:
03/12/2020 - 23:44