This session dedicated to a review of all different numerical methods students learned from this course.

Session 36: Final Lecture

This lecture finished up on the topic of stochastic chemical kinetics method.

Session 35: Stochastic Chemical Kinetics 2

An example of using the kinetic Monte Carlo method was demoed in class.

Session 34: Stochastic Chemical Kinetics 1

Students continued to learn more about Monte Carlo method and further learned about stochastic chemical kinetics in this lecture.

Session 33: Monte Carlo Methods 2

This lecture continued on models vs. data and learned more about the importance of consistency between models and data. Later the lecture changed the topic to operator splitting.

Session 30: Models vs. Data 3

Students learned basics in modeling for data analysis and also how to build valid models to tackle chemical engineering problems.

Session 28: Models vs. Data 1

This lecture continued in fundamental probability theory applied in the field of chemical engineering and moved on to the topic of data modeling later on.

Session 27: Probability Theory 2

This lecture finished up the topic of partial differential equations and moved on to probability theory.

Session 26: Partial Differential Equations 2

This is a review session to help prepare the quiz. Later, students moved on to learn finite volume methods and constructing simulations of PDEs.

Session 25: Review Session

Students learned to solve partial differential equations in this lecture.

Session 22: Partial Differential Equations 1

Numerical Methods Applied to Chemical Engineering

Students learned numerical methods to solve ordinary differential equation with boundary value problems.

Session 20: Boundary Value Problem 1

Quiz 1 result was announced. Later the lecture continued in solving differential algebraic equations.

Session 18: Differntial Algebraic Equations 2

Topics continued on solving problems of ordinary differential equation with initial value. Also introduced concept of functionals and nesting functionals.

Session 16: ODE-IVP and Numerical Integration 4

This lecture covered the topics on ordinary differential equation with initial value problem (ODE-IVP) and numerical integration.

Session 13: ODE-IVP and Numerical Integration 1

Students learned how to solve unconstrained optimization problems. In addition of the Newton-Raphson method, students also learned the steepest decent method, as well as the Trust-Region method.

Session 11: Unconstrained Optimization; Newton-Raphson and Trust Region Methods

This lecture summarized what students have learned on linear algebra and systems of nonlinear equations.

Session 9: Homotopy and Bifurcation

This lecture talked about the system of non-linear equations.

Session 7: Solutions of Nonlinear Equations; Newton-Raphson Method

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