Course content

Basis module. Functions of several variables. Partial differentiation, gradient. Critical points and optimization. Taylor’s theorem with remainder. Introduction to partial differential equations: examples and solutions.

Partial differential equations. Different types required different approaches, focus on physical/modeling intuition. Overview of the field. Steady state equations. Examples: Laplace’s and Poisson’s equation. Solution by computer using linear algebra. Examples: Heat equation, advection equation, wave equation. Solution by computer.

Programme module. Set theory. Set operations and terminology including intersection and union, Venn diagrams. Propositional logic. Propositions, connectives, disjunctive normal form. Predicate logic and quantifiers. Selected methods of proof. Inference rules and admissible rules. Basic number theory, modular arithmetic, and selected algorithms. Basic graph theory. Important graph types, including trees, and related algorithms such as breadth-first and depth-first search.

Learning outcome

Knowledge

The candidate has good knowledge of:

• Functions of several variables, including partial derivatives and their application to classification of stationary points and optimization.
• Taylor’s theorem and approximation by Taylor series.
• Partial differential equations, their properties and applications.
• Basic concepts and methods from propositional and predicate logic and set theory
• Selected forms of mathematical proof
• Basic number theory and modular arithmetic
• Terminology and selected algorithms for graphs.
• Digital tools for analysis of mathematical problems.

Abilities

The candidate can:

• Find and interpret the partial derivatives of a function of several variables
• Approximate functions by Taylor’s theorem and estimate the error with a remainder term.
• Solve simple optimization problems with several variables.
• Verify that a given function solves a partial differential equation
• Solve certain partial differential equations by computer, verify and interpret the results.
• Apply basic concepts, results and methods from logic and set theory, for example discern whether an argument is valid or not, or decide whether propositions are equivalent.
• Construct simple mathematical proofs
• Apply selected algorithms from basic number theory.
• Apply basic concepts and results related to graphs and apply selected algorithms to small examples.
• Apply digital tools to analyse mathematical problems.

General competence

The candidate:

• Has good knowledge of, and can apply a symbolic and formulaic mathematical apparatus that is relevant for communication in engineering sciences
• Has experience with applications of mathematical methods and digital tools to problems with their own and related specializations.
• Can connect mathematical concepts and techniques to models the candidate meets within and outside of their studies.

Learning methods and activities

Lectures, exercises and a project.

Tasks require both analytical and numerical methods with the use of digital tools.

Compulsory assignments

• Exercises
• Preproject

Specific conditions

Recommended previous knowledge

Mathematics for engineering 1 or similar

An introductory course in Python

Course materials

Recommended course material will be announced at the start of the semester

Credit reductions