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FSF3581 Computational Methods for Stochastic Differential Equations 7.5 credits

About course offering

For course offering

Spring 2024 Start 16 Jan 2024 programme students

Target group

Only PhD students

Part of programme

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P3 (4.0 hp), P4 (3.5 hp)


16 Jan 2024
3 Jun 2024

Pace of study


Form of study

Normal Daytime

Language of instruction


Course location

KTH Campus

Number of places

Places are not limited

Planned modular schedule


For course offering

Spring 2024 Start 16 Jan 2024 programme students

Application code



For course offering

Spring 2024 Start 16 Jan 2024 programme students


Anders Szepessy (, Mattias Sandberg (


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Course coordinator

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Headings with content from the Course syllabus FSF3581 (Spring 2023–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course treats stochastic differential equations and their numerical solution, with applications in financial mathematics, turbulent diffusion, control theory and Monte Carlo methods. We discuss basic questions for solving stochastic differential equations, e.g. to determine the price of an option is it more efficient to solve the deterministic Black and Scholes partial differential equation or use a Monte Carlo method based on stochastics.

The course treats basic theory of stochastic differential equations including weak and strong approximation, efficient numerical methods and error estimates, the relation between stochastic differential equations and partial differential equations, stochastic partial differential equations, variance reduction.

Intended learning outcomes

After completing this master level course the students will be able to model, analyze and efficiently compute solutions to problems including random phenomena in science and engineering. The student learns the basic mathematical theory for stochastic differential equations and optimal control and applies it to some real-world applications, including financial mathematics, material science, geophysical flow, radio networks, optimal design, optimal reconstruction, and chemical reactions in cell biology.

More precisely the goal of the course means that the student can:

  • present some models in science and finance based on stochastic differential equations and evaluate methods to determine their solution,

  • derive and use the correspondence between expected values of stochastic diffusion processes and solutions to certain deterministic partial differential equations,

  • formulate, use and analyze the main numerical methods for stochastic differential equations, based on Monte Carlo stochastics and partial differential equations,

  • present some stochastic and deterministic optimal control problems in science and finance using differential equations and Markov chains,

  • formulate, use and analyze deterministic and stochastic optimal control problems using both differential equations constrained minimization and dynamic programming (leading to the Hamilton-Jacobi-Bellman nonlinear partial differential equation),

  • derive the Black-Scholes equation for options in mathematical finance and analyze the alternatives to determine option prices numerically,

  • determine and analyze reaction rate problems for stochastic differential equation with small noise using optimal control theory.

Literature and preparations

Specific prerequisites

The prerequisite for the course is linear algebra, calculus, differential equations, probability and numerics corresponding to the first three years at KTH.

Recommended prerequisites

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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F


  • LAB1 - Laboratory work, 3.5 credits, grading scale: P, F
  • TEN1 - Written exam, 4.0 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

The course includes lectures, collaborative homework problems and student presentations of their computations inspired by recent research papers.

The homework and projects give credit points for a final written exam.

Other requirements for final grade

Laborations completed
Written exam completed

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

No information inserted


Anders Szepessy (, Mattias Sandberg (

Postgraduate course

Postgraduate courses at SCI/Mathematics