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Course

ChE-312: Numerical methods

Summary

This course introduces students to modern computational and mathematical techniques for solving problems in chemistry and chemical engineering. The use of introduced numerical methods will be demonstrated using the Python programming language.

Lectures

Instructors (2)

Kevin Sivula

Ljubisa Miskovic

Ljubisa Miskovic earned his Ph.D. degree in Automatic Control from the Swiss Federal Institute of Technology (EPFL) under the co-supervision of Dominique Bonvin and Alireza Karimi, in 2006. He pursued his postdoctoral studies at the Centre for Systems Engineering and Applied Mechanics, Universite Catholique de Louvain with Michel Gevers before moving to the laboratory of Vassily Hatzimanikatis at the EPFL. In 2010, he became a research scientist. His research interests include systems biology, metabolic engineering, synthetic biology, data-driven control design, system identification, stochastic processes and estimation theory.

Official source

https://edu.epfl.ch/coursebook/en/numerical-methods-CHE-312

Moodle Page

http://go.epfl.ch/ChE-312

About this result

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Summary

Lectures

Instructors (2)

Kevin Sivula

Ljubisa Miskovic

Official source

https://edu.epfl.ch/coursebook/en/numerical-methods-CHE-312

Moodle Page

http://go.epfl.ch/ChE-312

About this result

Ontological neighbourhood

Mathematics

Analysis: Numerical analysis, Differential anaysis

Computer engineering

Computer data storage: File systems

Lectures in this course (30)

MATLAB Essentials: Functions and Variables

Covers essential MATLAB functions, variables, loops, and debugging tools.

Direct Methods for Linear Systems of Equations

Explores direct methods for solving linear systems of equations, including Gauss elimination and LU decomposition.

Numerical Differentiation: Part 1

Covers numerical differentiation, forward differences, Taylor's expansion, Big O notation, and error minimization.

Numerical Differentiation: Backward and Central Differences

Explores backward and central differences for numerical differentiation, analyzing their properties and error analysis.

Numerical Differentiation: Richardson Extrapolation

Covers Richardson extrapolation for numerical differentiation to reduce error.

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