Lehrstuhl für Angewandte Mathematik

Cauerstraße 11
91058 Erlangen

Untergeordnete Organisationseinheiten

Professur für Angewandte Mathematik
Professur für Angewandte Mathematik
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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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)

Efficient Numerical Methods for Large Partial Differential Complementarity Systems arising in Multispecies Reactive Transport with Minerals in Porous Media
Prof. Dr. Peter Knabner; PD Dr. Serge Kräutle
(01.01.2007 - 31.12.2011)

Publikationen (Download BibTeX)

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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.
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
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). Dynamic inverse problems: modelling-regularization-numerics Preface. Inverse Problems, 34(4). https://dx.doi.org/10.1088/1361-6420/aab0f5
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
Burger, M. (2018). Joint reconstruction via coupled Bregman iterations with applications to PET-MR imaging. Inverse Problems, 34(1). https://dx.doi.org/10.1088/1361-6420/aa9425
Burger, M. (2018). Modern regularization methods for inverse problems. Acta Numerica, 27, 1-111. https://dx.doi.org/10.1017/S0962492918000016
Burger, M. (2018). SORTING PHENOMENA IN A MATHEMATICAL MODEL FOR TWO MUTUALLY ATTRACTING/REPELLING SPECIES. SIAM Journal on Mathematical Analysis, 50(3), 3210-3250. https://dx.doi.org/10.1137/17M1125716
Rupp, A., Knabner, P., & Dawson, C. (2018). A local discontinuous Galerkin scheme for Darcy flow with internal jumps. Computational Geosciences, 22(4), 1149-1159. https://dx.doi.org/10.1007/s10596-018-9743-7
Knodel, M. (2018). Virtual reality in advanced medical immersive imaging: a workflow for introducing virtual reality as a supporting tool in medical imaging. Computing and Visualization in Science, 18(6), 203-212. https://dx.doi.org/10.1007/s00791-018-0292-3
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
Wacker, P.K., Blömker, D., & Schillings, C. (2018). A STRONGLY CONVERGENT NUMERICAL SCHEME FROM ENSEMBLE KALMAN INVERSION. SIAM Journal on Numerical Analysis, 56(4), 2537-2562. https://dx.doi.org/10.1137/17M1132367
Aizinger, V., Rupp, A., Schütz, J., & Knabner, P. (2018). Analysis of a mixed discontinuous Galerkin method for instationary Darcy flow. Computational Geosciences, 22(1), 179-194. https://dx.doi.org/10.1007/s10596-017-9682-8
Hajduk, H., Hodges, B.R., Aizinger, V., & Reuter, B. (2018). Locally Filtered Transport for computational efficiency in multi-component advection-reaction models. ENVIRON MODELL SOFTW , 102, 185-198. https://dx.doi.org/10.1016/j.envsoft.2018.01.003
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
Marx, A., Conrad, M., Aizinger, V., Prechtel, A., van Geldern, R., & Barth, J. (2018). Groundwater data improve modelling of headwater stream CO2 outgassing with a stable DIC isotope approach. Biogeosciences, 15(10), 3093-3106. https://dx.doi.org/10.5194/bg-15-3093-2018

Zuletzt aktualisiert 2019-24-04 um 10:19