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Department of Mathematics, University of Oslo

Welcome to my homepage! I work as a Professor in Fluid Mechanics at the Department of Mathematics, University of Oslo. My primary interests are Computational Fluid Dynamics (CFD), scientific computing and the many aspects of turbulent fluid flows, ranging from physics, modeling, numerical methods, software implementation and applications. I spend part of my time within the 4DSpace strategic research initiative, where we study instabilities and turbulence in the polar ionosphere. I am also a Python enthusiast and I use this language as often as possible in teaching and scientific computing. Lately I have become very interested in scientific machine learning and its applications to fluid mechanics.

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References
  1. Mortensen, M. (2023). A Generic and Strictly Banded Spectral Petrov–Galerkin Method for Differential Equations with Polynomial Coefficients. SIAM Journal on Scientific Computing, 45(1), A123–A146. 10.1137/22M1492842
  2. Vasanth, J., Rabault, J., Alcántara-Ávila, F., Mortensen, M., & Vinuesa, R. (2024–12). Multi-agent Reinforcement Learning for the Control of Three-Dimensional Rayleigh–Bénard Convection. Flow, Turbulence and Combustion. 10.1007/s10494-024-00619-2
  3. Friedemann, C., Mortensen, M., & Nossen, J. (2021). Two-phase co-current flow simulations using periodic boundary conditions in horizontal, 4, 10 and 90° inclined eccentric annulus, flow prediction using a modified interFoam solver and comparison with experimental results. International Journal of Heat and Fluid Flow, 88, 108754. https://doi.org/10.1016/j.ijheatfluidflow.2020.108754
  4. Friedemann, C., Mortensen, M., & Nossen, J. (2019). Gas–liquid slug flow in a horizontal concentric annulus, a comparison of numerical simulations and experimental data. International Journal of Heat and Fluid Flow, 78, 108437. 10.1016/j.ijheatfluidflow.2019.108437
  5. Darian, D., Marholm, S., Mortensen, M., & Miloch, W. J. (2019). Theory and simulations of spherical and cylindrical Langmuir probes in non-Maxwellian plasmas. Plasma Physics and Controlled Fusion, 61(8), 085025. 10.1088/1361-6587/ab27ff
  6. Mortensen, M., Dalcin, L., & Keyes, D. (2019). mpi4py-fft: Parallel Fast Fourier Transforms with MPI for Python. Journal of Open Source Software, 4(36), 1340. 10.21105/joss.01340
  7. Bergersen, A. W., Mortensen, M., & Valen-Sendstad, K. (2019). The FDA nozzle benchmark: “In theory there is no difference between theory and practice, but in practice there is.” International Journal for Numerical Methods in Biomedical Engineering, 35(1), e3150. 10.1002/cnm.3150
  8. Miloch, W. J., Jung, H., Darian, D., Greiner, F., Mortensen, M., & Piel, A. (2018). Dynamic ion shadows behind finite-sized objects in collisionless magnetized plasma flows. New Journal of Physics, 20(7), 073027. 10.1088/1367-2630/aad066
  9. Darian, D., Marholm, S., Paulsson, J. J. P., Miyake, Y., Usui, H., Mortensen, M., & Miloch, W. J. (2017). Numerical simulations of a sounding rocket in ionospheric plasma: Effects of magnetic field on the wake formation and rocket potential. Journal of Geophysical Research: Space Physics, 122(9), 9603–9621. 10.1002/2017JA024284
  10. Haga, P. T., Pizzichelli, G., Mortensen, M., Kuchta, M., Pahlavian, S. H., Sinibaldi, E., Martin, B. A., & Mardal, K.-A. (2017). A Numerical Investigation of Intrathecal Isobaric Drug Dispersion Within the Cervical Subarachnoid Space. PLOS ONE, 12(3), 1–21. 10.1371/journal.pone.0173680
  11. Kuchta, M., Nordaas, M., Verschaeve, J. C. G., Mortensen, M., & Mardal, K.-A. (2016). Preconditioners for Saddle Point Systems With Trace Constraints Coupling 2D and 1D Domains. SIAM Journal on Scientific Computing, 38(6), B962–B987. 10.1137/15M1052822