Postgraduate Certificate in Computational Mathematics Modeling and Simulation

• Advanced Numerical Analysis: This unit covers the development and analysis of numerical methods for solving mathematical problems, with a particular focus on linear and nonlinear equations, interpolation and approximation, numerical differentiation and integration, and numerical solutions of ordinary and partial differential equations.
• High-Performance Computing: This unit introduces the principles and practices of high-performance computing, including parallel and distributed computing, cluster and grid computing, and cloud computing. Students will learn how to design, implement, and optimize numerical algorithms for high-performance computing platforms.
• Computational Mathematics for Data Science: This unit covers the application of computational mathematics techniques to data science problems, including data analysis, modeling, simulation, and visualization. Topics covered may include linear algebra, optimization, machine learning, and statistical modeling.
• Computational Fluid Dynamics: This unit introduces the principles and practices of computational fluid dynamics, including the Navier-Stokes equations, finite difference, finite volume, and finite element methods, and turbulence modeling. Students will learn how to design, implement, and validate numerical simulations of fluid flow problems.
• Computational Linear Algebra: This unit covers the application of computational mathematics techniques to linear algebra problems, including matrix factorization, decomposition, and solving linear systems. Students will learn how to design, implement, and optimize numerical algorithms for solving large-scale linear algebra problems.
• Computational Optimization: This unit introduces the principles and practices of computational optimization, including linear and nonlinear programming, integer programming, and evolutionary algorithms. Students will learn how to design, implement, and optimize numerical algorithms for solving complex optimization problems.
• Computational Mathematics for Partial Differential Equations: This unit covers the application of computational mathematics techniques to partial differential equations, including finite difference, finite volume, and finite element methods, and numerical solutions of elliptic, parabolic, and hyperbolic partial differential equations.
• Computational Science and Engineering: This unit introduces the principles and practices of computational science and engineering, including modeling, simulation, visualization, and uncertainty quantification. Students will learn how to design, implement, and validate numerical simulations of complex physical and engineering systems.