Applied Mathematics (TIMA)

Applied mathematics is used to study advanced methods for modeling in technology as well as natural and social sciences. The Division of Applied Mathematics (TIMA) conducts research in computational mathematics, mathematical statistics and optimization.

Job Opportunities

Subject areas at the Division of Applied Mathematics (TIMA)

Computational Mathematics

Computational mathematics develops and analyses numerical methods and algorithms for the solution of problems in science and engineering.

Important topics are well-posedness of the governing partial differential equations and convergence of the numerical approximation. Accuracy, stability, efficiency, software aspects and computer implementation are important.

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Mathematical Statistics

Mathematical statistics is the science of randomness and probabilities.

The research within the subject is divided into probability theory and statistical inference, where statistical inference (how to draw conclusions from random data) is based on probability theory.




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Optimization

Optimization aims at finding the best solutions to difficult problems.

All large-scale and complex operations must be planned, especially where cost is a factor. In many cases, the problems are too difficult to solve for a human being. A common property for many of these problems is that they give large, difficult combinatorial models that require research to be resolved.

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Research areas at the Division of Applied Mathematics (TIMA)

Abstract 3D pattern.

Modern Multivariate Statistical Analysis

Nowadays there is a great need to analyse complex high-dimensional data. Modern theories must be developed through the knowledge of the classical methods of multivariate statistics.

Unstructured mesh around high lift configuration.

Numerical Solutions of Time-Dependent Partial Differential Equations

Well-posedness of the governing partial differential equations lead to effective and accurate numerical methods for the analysis of physical processes in science and engineering.

A meeting in a modern officespace.

Mathematics and algorithms for intelligent decision-making

On the journey towards sustainability, our contribution is to develop mathematical models and solution methods for practically relevant but computationally challenging problems in scheduling and resource allocation.

A SJ-train driving through a beautiful landscape.

Decision support for railway crew planning

For our society to fully reap the benefits of sustainable train travel, careful resource planning is essential. Passengers need to be able to rely on that trains are on time and train operators need to make efficient use of their vehicles and crew.

Snow removal with a tractor in Stockholm.

Optimization of Snow Removal in Cities

Snow removal in a city is a major undertaking that requires route planning and efficient driving schedules. It is a difficult and complex optimization problem and research is needed to be able to get good solutions.

Flags of countries taking part in the research collaboration.

Research collaboration in Mathematics with low income countries and regions

The Department of Mathematics contributes to the development of capacity for higher education and research in low income countries and regions through collaborative projects in Africa and Asia.

Doctoral studies in Mathematics

Contacts at the Division of Applied Mathematics (TIMA)

Address

Visiting address

Department of Mathematics, B Building, entrance 21-25, Campus Valla

Postal address

Linköping University
Department of Mathematics
581 83 Linköping
Sweden

Seminars

Conference

New Publications

2024

Patrick Ersing, Andrew Ross Winters (2024) An Entropy Stable Discontinuous Galerkin Method for the Two-Layer Shallow Water Equations on Curvilinear Meshes Journal of Scientific Computing, Vol. 98, Article 62 Continue to DOI
Patrick Ersing, Andrew R. Winters (2024) An Entropy Stable Discontinuous Galerkin Method for the Two-Layer Shallow Water Equations on Curvilinear Meshes Journal of Scientific Computing, Vol. 98, Article 62 Continue to DOI
Viktor Linders, Mark H. Carpenter, Jan Nordström (2024) A superconvergent stencil-adaptive SBP-SAT finite difference scheme Journal of Computational Physics, Vol. 501, Article 112794 Continue to DOI
Stefane Saize, Xiangfeng Yang (2024) On the definitions of hidden Markov models Applied Mathematical Modelling, Vol. 125, p. 617-629 Continue to DOI
Jörg-Uwe Löbus (2024) Quasi-invariance under flows generated by non-linear PDEs Analysis and Applications, Vol. 22, p. 179-277 Continue to DOI
Jan Glaubitz, Jan Nordström, Philipp Öffner (2024) Energy-Stable Global Radial Basis Function Methods on Summation-By-Parts Form Journal of Scientific Computing, Vol. 98, Article 30 Continue to DOI
Tomas Lundquist, Andrew Ross Winters, Jan Nordström (2024) Encapsulated generalized summation-by-parts formulations for curvilinear and non-conforming meshes Journal of Computational Physics, Vol. 498, Article 112699 Continue to DOI
Alexander Rothkopf, Jan Nordström (2024) A symmetry and Noether charge preserving discretization of initial value problems Journal of Computational Physics, Vol. 498, Article 112652 Continue to DOI
Jan Nordström (2024) Nonlinear Boundary Conditions for Initial Boundary Value Problems with Applications in Computational Fluid Dynamics Journal of Computational Physics, Vol. 498, Article 112685 Continue to DOI

2023

Jennifer Chepkorir, Fredrik Berntsson, Vladimir Kozlov (2023) Solving stationary inverse heat conduction in a thin plate Partial Differential Equations and Applications, Vol. 4 Continue to DOI