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Department of Information Technology

Doctoral courses in Scientific Computing

Introduction

The doctoral subject of Scientific Computing is based on four mandatory courses that are offered regularly. Typically, two of them are offered each year. In addition to the mandatory courses, other courses and seminars are organized to cover special issues and timely material.

The individual curriculum for a doctoral student should contain both the mandatory courses and additional courses of particular relevance for his/her research project. Finally, it is emphasized that the curriculum should not only contain courses and seminars. Some portion of the curriculum has to be an individual reading of relevant literature.

General

Courses offered in 2024

course broad start teacher
Simulation techniques, 7.5hp yes Spring, 2024 Sandra May, Murtazo Nazarov and Ken Mattsson
Numerical Linear Algebra and Optimization, 7.5hp yes Period 2 autumn 2024 Roman Iakymchuk, Prashant Singh, and Martin Almquist

Mandatory courses

course abbreviation credits description
Mathematical foundations of scientific computing MF 7,5 credits Milestones in functional analysis and approximation theory with relevance to Neural Networks, basics of probability theory.
Simulation technologies ST 7,5 credits Deterministic and stochastic techniques to perform model simulations.
Numerical linear algebra with applications NLA 7,5 credits Fundamental matrix factorization techniques and their efficient computation; subspace iteration methods and acceleration techniques, (non-)randomized algorithms with emphasis on big data.
Techniques and technologies for scientific software engineering TTSSE 7,5 credits Shared memory parallelism, testing practices, reproducible experiments and computational environments, version control and code sharing, maintaining code over time, licensing concerns.

Tentative time-plan for mandatory courses

2023 2024 2025 2026
MF ST MF ST
TTSSE NLA TTSSE NLA

Other courses

Courses given regularly

course broad last instance periodicity (years)
Mathematical and numerical techniques for Partial Differential Equations (PDE), 10 hp yes 2023 3-4
Numerical Functional Analysis (NFA), 5 hp no 2022 3-4
Numerical Linear Algebra (NLA), 7.5 hp yes 2021 3-4
Numerical Methods for ODE (ODE), 7.5 hp yes 2020 3-4
Numerical Optimization (NO), 7.5 hp no 2021 3-4
Parallel Algorithms for Scientific Computing (PASC), 5 hp yes 2020 3-4
Parallel Programming for Scientific Computing (PPSC), 5 hp yes 2022 3-4

Relevant master level courses

course
Advanced Numerical Methods, 10 hp
Applied Finite Element Methods, 5 hp
Large Datasets for Scientific Applications, 5 hp
Applied Cloud Computing, 10 hp

Summer/Winter schools

school periodicity latest next
Collegio Puteano, Scuola Normale Superiore Unknown 2019
Hartree Unknown 2016
Montestigliano Annualish 2014
Rythms and oscillations Unknown 2014
Zürich summer school Biannual 2014
Jyväskylä Annual 2015
Dobbiaco summer school Annual 2015
Gene Golub summer school Annual 2015 Jul 25-Aug 5, 2016
Fluid Dynamics of Sustainability and the Environment Annual 2015 Sep 5-16, 2016
UPMARC Annual 2015
High-Order Finite Element and Isogeometric Methods Annual 2014
Rome-Moscow school of Matrix Methods and Applied Linear Algebra Annual 2014
International Winter School on Big Data 2015
Oberwolfach seminars Few times a year
Franco-German Summer School on Inverse Problems and Imaging in Bremen Sep 18-22, 2017
Data Science in Göttingen Jul 10-21 2017

Other relevant course sites

Previous courses

Courses given previously on a more regular basis

course latest year offered teacher
Classical Articles in Numerical Analysis (CA), 7.5 hp yes September Ken Mattsson
Finite Element Methods III, 7.5 hp 2010 Axel Målqvist
Iterative solution methods for nonlinear problems, 4.5 hp 2003 Maya Neytcheva
Mathematical Models and Numerical Methods for Fluid Mechanics, 6 hp 2015 Mattias Liefvendahl
Numerical Acoustics, 7.5 hp 2012 Ken Mattsson
Numerical Methods for Nonlinear Hyperbolic PDE, 5 hp 2015 Gunilla Kreiss
Perturbation theory and asymptotic expansions, 5 hp 2014 Elisabeth Larsson
Uncertainty Quantification, 5 hp 2015 Per Lötstedt
Advanced statistical computing (CIM), 5 hp 2015 Behrang Mahjani and Carl Nettelblad
Maximizing performance in practical HPC applications (SeSE) 2015 Maya Neytcheva and Carl Nettelblad
Applied Cloud Computing (SeSE), 5 hp 2016 Andreas Hellander and Salman Toor
Numerical Methods in Stochastic Modelling and Simulations (CIM), 7.5 hp 2016 Stefan Engblom and Josef Höök
Matrix Computations in Statistics with Applications (SeSE) 2016 Maya Neytcheva
Approximation theory, 7.5 hp 2017 Elisabeth Larsson and Olof Runborg

(SeSE) In collaboration with Swedish e-Science Education
(CIM) In collaboration with Centre for Interdisciplinary Mathematics

Courses offered in 2023

course broad start teacher
Techniques and Technologies for Scientific Software Engineering (TTSSE), 7.5 hp yes Spring, 2023 Carl Nettelblad, Sven-Erik Ekström
Finite Element Method, 7.5 hp no August 31, 2023 Murtazo Nazarov
Mathematical and Numerical Techniques for Partial Differential Equations (PDE), 7.5 hp yes Autumn 2023 Sandra May
Mathematical foundations for computational science, 7.5 hp yes Sep 2023 Gunilla Kreiss, Elisabeth Larsson, Mohammad Motamed, Stefan Engblom

Courses offered in 2022

course broad start teacher
Crash course on mathematics of deep learning, 5 hp yes May-June, 2022 Mohammad Motamed
Parallel programming for scientific computing, 5 hp yes February, 2022 Martin Kronbichler
Numerical Functional Analysis (NFA), 5 or 7.5 hp no Spring, 2022 Stefan Engblom
Finite Element Method, 7.5 hp no August 31, 2022 Murtazo Nazarov

Courses offered in 2021

course broad start teacher
Numerical Linear Algebra (NLA), 7.5 hp yes Spring 2021 Maya Neytcheva
Discontinuous Galerkin (DG) methods for hyperbolic problems, 5 hp no March 24, 2021 Jeniffer Ryan
Numerical Optimization, 7.5 hp no October 1, 2021 Ken Mattsson
Finite Element Method, 7.5 hp no August 31, 2021 Murtazo Nazarov

Courses offered in 2020

course broad start teacher
Finite Element Method, 7.5 hp no August 31, 2020 Murtazo Nazarov
Numerical Methods for ODE, 7.5 hp yes September 8, 2020 Gunilla Kreiss
SeSE course on Matrices in Statistics with Applications, 5 hp yes September 14-18, 2020 Maya Neytcheva
Parallel Algorithms for Scientific Computing PASC, 5~7.5 hp no November 2, 2020 Maya Neytcheva
Numerical Methods in Stochastic Modelling and Simulations, 7.5 hp yes January 2020 Stefan Engblom
SeSE course on Machine Learning, 7.5 hp yes May 2020 Salman Toor, Andreas Hellander, Carl Nettelblad

Courses offered in 2019

course broad start teacher
Finite Element Method, 7.5 hp no September Murtazo Nazarov
Mathematical and numerical techniques for Partial Differential Equations (PDE), 10 hp yes February Gunilla Kreiss
Numerical Functional Analysis (NFA), 5 hp no April 15 Stefan Engblom
Classical Articles in Numerical Analysis (CA), 7.5 hp yes September Ken Mattsson

Courses offered in 2018

course broad start teacher
Uncertainty Quantification, 5 hp or 7.5 hp yes Sep 6 Mohammad Motamed
Numerical linear algebra, 7.5 hp yes Oct 15 Maya Neytcheva
Parallel programming for scientific computing, 5 hp yes Dec 3 Sverker Holmgren
Applied Cloud SeSE, 5 hp yes Nov 5 Andreas Hellander

Courses offered in 2017

course start teacher
Approximation theory, 7.5 hp May 23 Elisabeth Larsson, Olof Runborg
Numerical optimization, 10 hp September 18 Ken Mattsson, Maya Neytcheva, and Prashant Singh
Parallel algorithms for scientific computing, 5 hp October or November Sverker Holmgren and Maya Neytcheva

Courses offered in 2016

course start teacher
Applied Cloud Computing (SeSE), 5 hp Period 3 Andreas Hellander and Salman Toor
Numerical Methods in Stochastic Modelling and Simulations (CIM), 7.5 hp Period 3 Stefan Engblom and Josef Höök
Matrix Computations in Statistics with Applications (SeSE) Period 3 Maya Neytcheva
Numerical methods for ODEs and DAEs, 7.5 hp (recent information and course syllabus) Period 1 Per Lötstedt, Michael Hanke
Research projects in Scientific Computing, 7.5 hp Period 2 Lina von Sydow

(SeSE) In collaboration with Swedish e-Science Education
(CIM) In collaboration with Centre for Interdisciplinary Mathematics

Updated  2024-01-16 10:04:01 by Lina von Sydow.