This module introduces first-order partial differential equations (PDEs). Students will learn how to classify and approach these equations using numerical methods to find solutions.
This module introduces the motivation behind the course content with practical examples that highlight the relevance of numerical methods in solving real-world problems. Students will learn how these methods are applied in various fields such as engineering, physics, and finance.
In this module, students will explore single-step methods for solving initial value problems (IVPs). The focus will be on understanding methods such as the Taylor series method, Euler method, and Picardâs method of successive approximations. Students will also learn how to derive finite difference equations and analyze stability.
This module delves into the analysis of single-step methods, examining the truncation error and convergence aspects. Students will learn to evaluate the accuracy of various numerical methods used in solving ordinary differential equations.
This module covers Runge-Kutta methods, which are popular techniques for solving ordinary differential equations. Students will learn about various orders of Runge-Kutta methods, their derivation, application, and advantages over simpler methods.
Higher order methods for solving differential equations are the focus of this module. Students will learn techniques for handling higher-order initial value problems and understand how to apply multi-step methods effectively.
In this module, students will study error analysis, stability, and convergence of single-step methods. Understanding these concepts is crucial for ensuring the reliability of numerical solutions in practical applications.
This module provides an opportunity for students to engage in practical tutorials that reinforce concepts learned in previous modules. Students will solve problems and discuss strategies for effective numerical methods.
Continuing from the previous tutorial, this module allows students to further practice numerical methods. It emphasizes collaborative learning and peer feedback to enhance understanding of the material.
This module explores multi-step methods for solving initial value problems, focusing on explicit methods. Students will learn how these methods can increase efficiency and accuracy in numerical computations.
In this module, students will learn about implicit multi-step methods for solving initial value problems. The discussion will include derivation, implementation, and comparisons with explicit methods.
This module focuses on convergence and stability of multi-step methods. Students will analyze conditions for convergence and learn techniques to ensure stability in numerical solutions.
General methods for absolute stability are discussed in this module. Students will learn how to assess stability across different numerical methods and the implications for solution accuracy.
This module delves into the stability analysis of multi-step methods. Students will learn how to apply stability criteria to ensure robust numerical solutions for differential equations.
The predictor-corrector method is the focus of this module. Students will learn about its application in solving initial value problems and the benefits it offers over other methods.
This module provides additional insights into multi-step methods. Students will engage in discussions on practical considerations, limitations, and best practices for applying these methods effectively.
Finite difference methods for linear boundary value problems are discussed in this module. Students will learn about the formulation and implementation of these techniques to solve practical problems.
This module examines linear and non-linear second-order boundary value problems. Students will learn comparison techniques and methods for solving these problems using numerical methods.
This module addresses boundary value problems with derivative boundary conditions. Students will learn specific techniques to handle these scenarios effectively in numerical computations.
Higher-order boundary value problems are explored in this module. Students will learn about the complexities these problems introduce and strategies for their numerical resolution.
The shooting method for boundary value problems is the focus of this module. Students will learn how to apply this technique effectively for solving differential equations with specific boundary conditions.
This module provides a third tutorial session, allowing students to practice their skills and solidify their understanding of previous topics in numerical methods.
This module introduces first-order partial differential equations (PDEs). Students will learn how to classify and approach these equations using numerical methods to find solutions.
In this module, students will explore second-order partial differential equations. They will examine various numerical methods used for solving these equations and their applications in real-world scenarios.
This module discusses finite difference approximations to parabolic PDEs. Students will learn how to develop and implement these methods for effective problem-solving.
Implicit methods for parabolic PDEs are the focus of this module. Students will analyze the advantages and challenges of using implicit techniques for solving differential equations.
This module examines the consistency, stability, and convergence of finite difference methods for parabolic PDEs. Students will learn how to ensure their numerical solutions are reliable and accurate.
In this module, students will explore other numerical methods for parabolic PDEs. Various techniques will be discussed, providing students with a broader toolkit for solving these types of problems.
This module provides a fourth tutorial session where students can apply their knowledge to practical problems, reinforcing their understanding of the course material.
This module covers matrix stability analysis of finite difference schemes. Students will learn how to assess the stability of their numerical methods using matrix techniques.
In this module, students will examine Fourier series stability analysis of finite difference schemes. This approach will provide further insights into ensuring stability in numerical computations.
This module focuses on finite difference approximations to elliptic PDEs. Students will learn how to formulate and solve these equations using numerical methods.
Continuing from the previous module, this section delves deeper into finite difference approximations for elliptic PDEs, enhancing students' skills in solving complex problems.
This module provides advanced techniques for finite difference approximations to elliptic PDEs. Students will learn about various methods and their applications in solving specific problems.
In this final module, students will consolidate their understanding of finite difference approximations to elliptic PDEs. They will review key concepts and engage in discussions on practical implications.
This module explores finite difference approximations for hyperbolic PDEs. Students will learn about the characteristics of these equations and the numerical techniques used to solve them effectively.
Continuing with hyperbolic PDEs, this module discusses advanced finite difference approximations and their applications. Students will engage with more complex scenarios to improve their problem-solving abilities.
This module introduces the method of characteristics for hyperbolic PDEs. Students will learn about its formulation and how to apply it to solve specific types of problems effectively.
In this module, students will continue to explore the method of characteristics for hyperbolic PDEs. They will engage in practical exercises that reinforce their understanding of this important technique.
This module discusses finite difference approximations to first-order hyperbolic PDEs. Students will learn the unique challenges posed by these equations and solutions through numerical methods.
In this concluding module, students will summarize their learning experiences throughout the course. They will review key concepts, methodologies, and practical applications of numerical methods in solving differential equations.