Radial basis function (RBF) approximations for PDE problems

The picture below is an example of how the RBFs can be visualized in a two-dimensional computational domain. Six weighted radial basis functions, drawn as red surfaces in the picture, are scattered over the computational domain. Their sum build up the interpolant, represented by the transparent surface with wireframe.

The main advantages of the RBF method are

• The method is meshfree, which means that it is flexible with respect to the geometry of the computational domain. It also means that the method is suitable for problems where data is only available at scattered points.
• The method is not more complicated for problems with many space dimensions, since the only geometrical property that is used is the pairwise distance between points.
• For smooth functions, approximations with smooth RBFs can give spectral convergence.

We regularly offer subjects for MSc thesis projects. Please consult our list of available projects. You may also contact us directly to discuss alternative topics.

Biomechanical simulation of the respiratory muscles

The focus is put to the simulation of the diaphragm, the main muscle of the respiratory system. The underlying model is based on the equations of nonlinear elasticity which are solved on a realistic 3-dimensional geometry using RBFs.

The flat RBF limit

Numerical investigations and theory concerning the limit where the RBFs become flat. This limit is interesting because it can produce very accurate results for smooth functions and it reproduces multivariate polynomial interpolation.

RBFs for PDEs

General algorithms and methods for solving PDE problems using RBFs.

RBFs for high-dimensional PDEs

Algorithms and methods specifically designed for high-dimensional application fields such as financial mathematics and quantum dynamics.

RBFs for global climate simulation

RBF methods for linear and non-linear applications in geophysics.

Various RBF codes, mostly in MATLAB are collected under the RBF software page.

The Research Group Enjoying the weather Mar 2022

Refereed

1. A first meshless approach to simulation of the elastic behaviour of the diaphragm. Nicola Cacciani, Elisabeth Larsson, Alberto Lauro, Marco Meggiolaro, Alessio Scatto, Igor Tominec, and Pierre-Frédéric Villard. In Spectral and High Order Methods for Partial Differential Equations: ICOSAHOM 2018, volume 134 of Lecture Notes in Computational Science and Engineering, pp 501-512, Springer, 2020. (DOI, fulltext:postprint).
2. Local meshless methods for second order elliptic interface problems with sharp corners. Masood Ahmad, Siraj-ul Islam, and Elisabeth Larsson. In Journal of Computational Physics, volume 416, 2020. (DOI, fulltext:postprint).
3. A high order method for pricing of financial derivatives using radial basis function generated finite differences. Slobodan Milovanovi? and Lina von Sydow. In Mathematics and Computers in Simulation, volume 174, pp 205-217, 2020. (DOI).
4. Tail distribution and extreme quantile estimation using non-parametric approaches. Imen Rached and Elisabeth Larsson. In High-Performance Modelling and Simulation for Big Data Applications, pp 69-87, Springer, 2019. (DOI, Fulltext).
5. BENCHOP–SLV: The BENCHmarking project in Option Pricing – Stochastic and local volatility problems. Lina von Sydow, Slobodan Milovanovi?, Elisabeth Larsson, Karel In't Hout, Magnus Wiktorsson, Cornelis W. Oosterlee, Victor Shcherbakov, Maarten Wyns, Alvaro Leitao, Shashi Jain, Tinne Haentjens, and Johan Waldén. In International Journal of Computer Mathematics, volume 96, pp 1910-1923, 2019. (DOI, Fulltext).
6. Radial basis function generated finite differences for option pricing problems. Slobodan Milovanovi? and Lina von Sydow. In Computers and Mathematics with Applications, volume 75, pp 1462-1481, 2018. (DOI).
7. Radial basis function methods for the Rosenau equation and other higher order PDEs. Ali Safdari-Vaighani, Elisabeth Larsson, and Alfa Heryudono. In Journal of Scientific Computing, volume 75, pp 1555-1580, 2018. (DOI, Fulltext).
8. Forward deterministic pricing of options using Gaussian radial basis functions. Jamal Amani Rad, Josef Höök, Elisabeth Larsson, and Lina von Sydow. In Journal of Computational Science, volume 24, pp 209-217, 2018. (DOI, fulltext:postprint).
9. A least squares radial basis function partition of unity method for solving PDEs. Elisabeth Larsson, Victor Shcherbakov, and Alfa Heryudono. In SIAM Journal on Scientific Computing, volume 39, pp A2538-A2563, 2017. (DOI).
10. An adaptive interpolation scheme for molecular potential energy surfaces. Markus Kowalewski, Elisabeth Larsson, and Alfa Heryudono. In Journal of Chemical Physics, volume 145, pp 084104:1-10, 2016. (DOI).
11. Radial basis function partition of unity methods for pricing vanilla basket options. Victor Shcherbakov and Elisabeth Larsson. In Computers and Mathematics with Applications, volume 71, pp 185-200, 2016. (DOI, fulltext:postprint).
12. Preconditioning for radial basis function partition of unity methods. Alfa Heryudono, Elisabeth Larsson, Alison Ramage, and Lina von Sydow. In Journal of Scientific Computing, volume 67, pp 1089-1109, 2016. (DOI, fulltext:postprint).
13. BENCHOP—The BENCHmarking project in Option Pricing. Lina von Sydow, Lars Josef Höök, Elisabeth Larsson, Erik Lindström, Slobodan Milovanovi?, Jonas Persson, Victor Shcherbakov, Yuri Shpolyanskiy, Samuel Sirén, Jari Toivanen, Johan Waldén, Magnus Wiktorsson, Jeremy Levesley, Juxi Li, Cornelis W. Oosterlee, Maria J. Ruijter, Alexander Toropov, and Yangzhang Zhao. In International Journal of Computer Mathematics, volume 92, pp 2361-2379, 2015. (DOI, fulltext:postprint).
14. A scalable RBF–FD method for atmospheric flow. Martin Tillenius, Elisabeth Larsson, Erik Lehto, and Natasha Flyer. In Journal of Computational Physics, volume 298, pp 406-422, 2015. (DOI, fulltext:postprint).
15. A radial basis function partition of unity collocation method for convection–diffusion equations arising in financial applications. Ali Safdari-Vaighani, Alfa Heryudono, and Elisabeth Larsson. In Journal of Scientific Computing, volume 64, pp 341-367, 2015. (DOI, fulltext:postprint).
16. A Galerkin radial basis function method for the Schrödinger equation. Katharina Kormann and Elisabeth Larsson. In SIAM Journal on Scientific Computing, volume 35, pp A2832-A2855, 2013. (DOI).
17. Stable computation of differentiation matrices and scattered node stencils based on Gaussian radial basis functions. Elisabeth Larsson, Erik Lehto, Alfa Heryudono, and Bengt Fornberg. In SIAM Journal on Scientific Computing, volume 35, pp A2096-A2119, 2013. (DOI).
18. Stable calculation of Gaussian-based RBF-FD stencils. Bengt Fornberg, Erik Lehto, and Collin Powell. In Computers and Mathematics with Applications, volume 65, pp 627-637, 2013. (DOI).
19. A guide to RBF-generated finite differences for nonlinear transport: Shallow water simulations on a sphere. Natasha Flyer, Erik Lehto, Sébastien Blaise, Grady B. Wright, and Amik St-Cyr. In Journal of Computational Physics, volume 231, pp 4078-4095, 2012. (DOI).
20. Radial basis functions for the time-dependent Schrödinger equation. Katharina Kormann and Elisabeth Larsson. In Numerical Analysis and Applied Mathematics: ICNAAM 2011, volume 1389 of AIP Conference Proceedings, pp 1323-1326, American Institute of Physics (AIP), Melville, NY, 2011. (DOI).
21. Stable computations with Gaussian radial basis functions. Bengt Fornberg, Elisabeth Larsson, and Natasha Flyer. In SIAM Journal on Scientific Computing, volume 33, pp 869-892, 2011. (DOI).
22. Stabilization of RBF-generated finite difference methods for convective PDEs. Bengt Fornberg and Erik Lehto. In Journal of Computational Physics, volume 230, pp 2270-2285, 2011. (DOI).
23. Rotational transport on a sphere: Local node refinement with radial basis functions. Natasha Flyer and Erik Lehto. In Journal of Computational Physics, volume 229, pp 1954-1969, 2010. (DOI).
24. A note on radial basis function interpolant limits. Martin D. Buhmann, Slawomir Dinew, and Elisabeth Larsson. In IMA Journal of Numerical Analysis, volume 30, pp 543-554, 2010. (DOI).
25. Multi-dimensional option pricing using radial basis functions and the generalized Fourier transform. Elisabeth Larsson, Krister Åhlander, and Andreas Hall. In Journal of Computational and Applied Mathematics, volume 222, pp 175-192, 2008. (DOI).
26. Improved radial basis function methods for multi-dimensional option pricing. Ulrika Pettersson, Elisabeth Larsson, Gunnar Marcusson, and Jonas Persson. In Journal of Computational and Applied Mathematics, volume 222, pp 82-93, 2008. (DOI).
27. A new class of oscillatory radial basis functions. Bengt Fornberg, Elisabeth Larsson, and Grady Wright. In Computers and Mathematics with Applications, volume 51, pp 1209-1222, 2006. (DOI).
28. Option pricing using radial basis functions. Ulrika Pettersson, Elisabeth Larsson, Gunnar Marcusson, and Jonas Persson. In Proc. ECCOMAS Thematic Conference on Meshless Methods, pp C24.1-6, Departamento de Matemática, Instituto Superior Técnico, Lisboa, Portugal, 2005.
29. Theoretical and computational aspects of multivariate interpolation with increasingly flat radial basis functions. Elisabeth Larsson and Bengt Fornberg. In Computers and Mathematics with Applications, volume 49, pp 103-130, 2005. (DOI).
30. Some observations regarding interpolants in the limit of flat radial basis functions. Bengt Fornberg, Grady Wright, and Elisabeth Larsson. In Computers and Mathematics with Applications, volume 47, pp 37-55, 2004. (DOI).
31. A numerical study of some radial basis function based solution methods for elliptic PDEs. Elisabeth Larsson and Bengt Fornberg. In Computers and Mathematics with Applications, volume 46, pp 891-902, 2003. (DOI).

PhD and Licentiate theses

1. Global radial basis function collocation methods for PDEs. Ulrika Sundin. Licentiate thesis, IT licentiate theses / Uppsala University, Department of Information Technology nr 2020-002, Uppsala University, 2020. (fulltext).
2. Radial Basis Function generated Finite Difference Methods for Pricing of Financial Derivatives. Slobodan Milovanovi?. Ph.D. thesis, Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology nr 1702, Acta Universitatis Upsaliensis, Uppsala, 2018. (fulltext, preview image).
3. Localised Radial Basis Function Methods for Partial Differential Equations. Victor Shcherbakov. Ph.D. thesis, Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology nr 1600, Acta Universitatis Upsaliensis, Uppsala, 2018. (fulltext, preview image).
4. Radial basis function methods for pricing multi-asset options. Victor Shcherbakov. Licentiate thesis, IT licentiate theses / Uppsala University, Department of Information Technology nr 2016-001, Uppsala University, 2016. (fulltext).

Supervised BSc and MSc theses

• Igor Tominec, Parallel localized radial basis function methods for the shallow water equations on the sphere, Technical University Munich, 2017. (Advisors: E. Larsson and H.-J. Bungartz) (Link: http://user.it.uu.se/~bette/IT_MasterThesis_final.pdf )

• Discretization of the Dirac delta function for application in option pricing. Håkan Öhrn and Adam Lindell. Student thesis, supervisor: Lina von Sydow, Elisabeth Larsson, examiner: Martin Sjödin, TVE nr 16 067 juni, 2016. (fulltext).
• Numerical methods for option pricing under the CGMY process. Gustav Ludvigsson. Student thesis, supervisor: Josef Höök, Lina von Sydow, examiner: Tomas Nyberg, Elisabeth Larsson, UPTEC F nr 15014, 2015. (fulltext).
• Evaluation of a least-squares radial basis function approximation method for solving the Black-Scholes equation for option pricing. Cong Wang. Student thesis, supervisor: Elisabeth Larsson, examiner: Lina von Sydow, Jarmo Rantakokko, IT nr 12 051, 2012. (fulltext).
• Adapting a Radial Basis Functions Framework for Large-Scale Computing. Afshin Zafari. Student thesis, supervisor: Elisabeth Larsson, examiner: Jarmo Rantakokko, IT nr 12 050, 2012. (fulltext).
• Parallelizing a Software Framework for Radial Basis Function Methods. Linus Sunde. Student thesis, supervisor: Martin Tillenius, Elisabeth Larsson, examiner: Michael Thuné, Anders Jansson, IT nr 11 084, 2011. (fulltext).
• Designing a Flexible Software Tool for RBF Approximations Applied to PDEs. Danhua Xiang. Student thesis (Master Programme in Computational Science), supervisor: Elisabeth Larsson, Martin Tillenius, examiner: Michael Thuné, Anders Jansson, IT nr 10 058, 2010. (fulltext).

• Björn Rodhe, A discontinuous Galerkin method with local radial basis function interpolation, UPTEC Report F 07 066, School of Engineering, Uppsala University, 2007. (Advisors: E. Larsson and S.-E. Ekström)
• Andreas Hall, Pricing financial derivatives using radial basis functions and the generalized Fourier transform, UPTEC Report IT 05 036, School of Engineering, Uppsala University, 2005. (Advisors: E. Larsson and K. Åhlander)
• Gunnar Marcusson, Option pricing using radial basis functions, UPTEC Report F 04 078, School of Engineering, Uppsala University, 2004. (Advisors: E. Larsson and L. von Sydow)
• Ulrika Pettersson, Radial basis function approximations for the Helmholtz equation, UPTEC Report F 03 082, School of Engineering, Uppsala University, 2003. (Advisor: E. Larsson)

• Elisabeth Larsson, Professor, Dept. of IT, Scientific Computing, Uppsala University.
• Lina von Sydow, Professor, Dept. of IT, Scientific Computing, Uppsala University.
• Katharina Kormann, Associate Professor, Dept. of IT, Scientific Computing, Uppsala University.
• Bostjan Mavric, Researcher, Dept. of IT, Scientific Computing, Uppsala University.
• Ulrika Sundin, M.Sc., Ph.D. student, Dept. of IT, Scientific Computing, Uppsala University.
• Igor Tominec, M.Sc., Ph.D. student, Dept. of IT, Scientific Computing, Uppsala University.
• Andreas Michael, M.Eng., Ph.D. student starting in Sep., Dept. of IT, Scientific Computing, Uppsala University.
• Bengisen Pekmen Geridönmez, Visiting Associate Professor, TED University, Ankara, Türkiye.
• Fatemeh Pooladi, Visiting Ph.D. student, Persian Gulf University, Bushehr, Iran.

Research group at Katalin, Feb 2011	In the Dolomites, Sep 2015

Still collaborators, but at a longer distance.

• Alfa Heryudono, Ph.D., Dept. of Mathematics, University of Massachusetts, Dartmouth, MA, USA (visiting researcher jun 2010-aug 2011).
• Ali Safdari-Vaighani, Ph.D., Allameh Tabatabai University, Tehran, Iran (visiting Ph.D. student 2011).
• Cecile Piret, Ph.D., Applied Mechanics and Mathematics (MEMA), Université Catholique de Louvain (UCL), Belgium (visiting researcher jul 2012-sep 2012).
• Erik Lehto, Ph.D., Numerical Analysis, KTH Royal Institute of Technology, Stockholm (PhD from Uppsala University 2012).
• Martin Tillenius, Ph.D., Machine games, Uppsala (PhD from Uppsala University in 2014).
• Ahmad Saeidi, Iran University of Science and Technology, Tehran, Iran (visiting PhD student 2015).
• Jamal Amani Rad, Ph.D., Shahid Beheshti University, Tehran, Iran (visiting PhD student 2015).
• Victor Shcherbakov, Ph.D., SEB, Stockholm (PhD from Uppsala University in 2018).
• Slobodan Milovanovic, Ph.D., Boston Consulting Group, Stockholm (PhD from Uppsala University in 2018).

• Martin Buhmann, Prof., Mathematical Institute, Justus-Liebig-Universität Giessen, Germany.
• Natasha Flyer, Ph.D., Div. of Scientific Computing, The National Center for Atmospheric Research (NCAR), Boulder, CO, USA.
• Bengt Fornberg, Prof., Dept. of Applied Mathematics, University of Colorado, Boulder, CO, USA.
• Sônia Gomes, Prof., Dept. of Applied Mathematics, University of Campinas, Brazil.
• Simon Hubbert, Ph.D., Dept. of Economics Mathematics and Statistics, Birckbeck college, University of London, London, UK.
• Amir Malekpour, Ph.D., Hydraulic Structure Engineering, University of Guilan, Iran.
• Alison Ramage, Reader, Dept. of Mathematics and Statistics, University of Strathclyde, Scotland.
• Robert Schaback, Prof., Institute for Numerical and Applied Mathematics, Georg-August-University Göttingen, Germany.
• Grady Wright, Ph.D., Dept. of Mathematics, University of Utah, Salt Lake City, UT, USA.

For more information contact Elisabeth Larsson.