Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik)

Cauerstraße 11
91058 Erlangen

Untergeordnete Organisationseinheiten

Professur für Angewandte Mathematik
Professur für Angewandte Mathematik (Analysis und Numerik partieller Differentialgleichungen)
Professur für Angewandte Mathematik (Mathematische Modellierung)


Multicomponent reactive transport in natural porous media
Multiscale modeling, analysis and simulation of reaction-diffusion processes in porous media. Application to carbohydrat
Geophysical free surface flows
Multiphase flow in natural porous media
Emergence in natural porous media
Stochastic modeling of transport processes in porous media


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DAAD Austauschprogramm: PPP Finnland 2017: Bayesian Inverse Problems in Banach Space
Prof. Dr. Martin Burger
(25.01.2015 - 31.12.2017)

LifeInverse: Variational Methods for Dynamic Inverse Problems in the Life Sciences
Prof. Dr. Martin Burger
(01.03.2014 - 28.02.2019)

Implementierung und Optimierung von Stencil-Operationen auf gestaffelten hierarchischen Gittern
Prof. Dr. Eberhard Bänsch; PD Dr. Nicolas Neuß
(01.06.2013 - 01.10.2014)

MPFA (Multi Point Flux Approximation) und gemischt-hybride Finite Element Methoden für Fluss und Transport in porösen Medien
Prof. Dr. Peter Knabner
(01.01.2012 - 31.12.2013)

Entwicklung neuer photokatalytischer Filtersysteme zur Luftreinigung von Nanopartikeln, organischen Zusätzen und Bakterien mit Hilfe numerischer Simulationen
Prof. Dr. Peter Knabner
(01.10.2009 - 30.09.2011)

Publikationen (Download BibTeX)

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Liu, C., Frank, F., Alpak, F.O., & Rivière, B. (2019). An interior penalty discontinuous Galerkin approach for 3D incompressible Navier–Stokes equation for permeability estimation of porous media. Journal of Computational Physics, 396, 669-686. https://dx.doi.org/10.1016/j.jcp.2019.06.052
Gahn, M., Neuss-Radu, M., & Knabner, P. (2018). EFFECTIVE INTERFACE CONDITIONS FOR PROCESSES THROUGH THIN HETEROGENEOUS LAYERS WITH NONLINEAR TRANSMISSION AT THE MICROSCOPIC BULK-LAYER INTERFACE. Networks and Heterogeneous Media, 13(4), 609-640. https://dx.doi.org/10.3934/nhm.2018028
Föcke, J., Baumgarten, D., & Burger, M. (2018). The inverse problem of magnetorelaxometry imaging. Inverse Problems, 34(11). https://dx.doi.org/10.1088/1361-6420/aadbbf
Föcke, J. (2018). SiMRX - A Simulation toolbox for MRX.
Mu, X., Frank, F., Rivière, B., Alpak, F.O., & Chapman, W.G. (2018). Mass-conserved density gradient theory model for nucleation process. Industrial & Engineering Chemistry Research. https://dx.doi.org/10.1021/acs.iecr.8b03389
Frank, F., Liu, C., Alpak, F.O., Berg, S., & Rivière, B. (2018). Direct numerical simulation of flow on pore-scale images using the phase-field method. Spe Journal, 23(5), 1–18. https://dx.doi.org/10.2118/182607-PA
Rupp, A., Totsche, K.U., Prechtel, A., & Ray, N. (2018). Discrete-Continuum Multiphase Model for Structure Formation in Soils Including Electrostatic Effects. Frontiers in Environmental Science, 6. https://dx.doi.org/10.3389/fenvs.2018.00096
Alpak, F.O., Samardžić, A., & Frank, F. (2018). A distributed parallel direct simulator for pore-scale two-phase flow on digital rock images using a finite difference implementation of the phase-field method. Journal of Petroleum Science and Engineering, 166, 806–824. https://dx.doi.org/10.1016/j.petrol.2017.11.022
Burger, M. (2018). Dynamic MRI reconstruction from undersampled data with an anatomical prescan. Inverse Problems, 34(7). https://dx.doi.org/10.1088/1361-6420/aac3af
Burger, M. (2018). Sparsity-promoting and edge-preserving maximum a posteriori estimators in non-parametric Bayesian inverse problems. Inverse Problems, 34(4). https://dx.doi.org/10.1088/1361-6420/aaacac
Burger, M. (2018). Dynamic inverse problems: modelling-regularization-numerics Preface. Inverse Problems, 34(4). https://dx.doi.org/10.1088/1361-6420/aab0f5
Bungert, L., Ehrhardt, M.J., Coomes, D., Rasch, J., Reisenhofer, R., & Schönlieb, C.-B. (2018). Blind image fusion for hyperspectral imaging with the directional total variation. Inverse Problems, 34(4). https://dx.doi.org/10.1088/1361-6420/aaaf63
Neuß, N. (2018). Interactive flow simulation with Common Lisp. In EPITA (Eds.), Proceedings of the European Lisp Symposium 2018 (pp. 78-79). Marbella.
Reips, L., Burger, M., & Engbers, R. (2018). Towards dynamic PET reconstruction under flow conditions: Parameter identification in a PDE model. Journal of Inverse and Ill-posed Problems, 26(2), 185-200. https://dx.doi.org/10.1515/jiip-2015-0016
Burger, M. (2018). Pattern formation of a nonlocal, anisotropic interaction model. Mathematical Models & Methods in Applied Sciences, 28(3), 409-451. https://dx.doi.org/10.1142/S0218202518500112
Fried, M., Aizinger, V., & Bungert, L. (2018). Comparison of two local discontinuous Galerkin formulations for the subjective surfaces problem. Computing and Visualization in Science. https://dx.doi.org/10.1007/s00791-018-0291-4
Totsche, K.U., Amelung, W., Gerzabek, M.H., Guggenberger, G., Klumpp, E., Knief, C.,... Kögel-Knabner, I. (2018). Microaggregates in Soils. Journal of Plant Nutrition and Soil Science, 181(1), 104-136. https://dx.doi.org/10.1002/jpln.201600451
Frank, F., Liu, C., Alpak, F.O., & Rivière, B. (2018). A finite volume/discontinuous Galerkin method for the advective Cahn–Hilliard equation with degenerate mobility on porous domains stemming from micro-CT imaging. Computational Geosciences, 22(2), 543–563. https://dx.doi.org/10.1007/s10596-017-9709-1
Himmelsbach, D., Neuss-Radu, M., & Neuß, N. (2018). Mathematical modelling and analysis of nanoparticle gradients induced by magnetic fields. Journal of Mathematical Analysis and Applications, 461(2), 1544-1560. https://dx.doi.org/10.1016/j.jmaa.2017.12.026
Jaust, A., Reuter, B., Aizinger, V., Schütz, J., & Knabner, P. (2018). FESTUNG: A MATLAB/GNU Octave toolbox for the discontinuous Galerkin method. Part III: Hybridized discontinuous Galerkin (HDG) formulation. Computers & Mathematics With Applications, 75(12), 4505-4533. https://dx.doi.org/10.1016/j.camwa.2018.03.045

Zuletzt aktualisiert 2019-11-07 um 23:51