Wolfgang Bangerth

Affiliations: 
Mathematics Texas A & M University, College Station, TX, United States 
Area:
Mathematics
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"Wolfgang Bangerth"
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Long DK, Bangerth W, Handwerk DR, et al. (2021) Estimating reaction parameters in mechanism-enabled population balance models of nanoparticle size distributions: A Bayesian inverse problem approach. Journal of Computational Chemistry
Ghesmati A, Bangerth W, Turcksin B. (2019) Residual-based a posteriori error estimation for hp-adaptive finite element methods for the Stokes equations Journal of Numerical Mathematics. 27: 237-252
Fraters MRT, Bangerth W, Thieulot C, et al. (2019) Efficient and practical Newton solvers for non-linear Stokes systems in geodynamic problems Geophysical Journal International. 218: 873-894
Gassmöller R, Lokavarapu H, Heien E, et al. (2018) Flexible and Scalable Particle‐in‐Cell Methods With Adaptive Mesh Refinement for Geodynamic Computations Geochemistry Geophysics Geosystems. 19: 3596-3604
Heister T, Dannberg J, Gassmöller R, et al. (2017) High accuracy mantle convection simulation through modern numerical methods – II: realistic models and problems Geophysical Journal International. 210: 833-851
Turcksin B, Kronbichler M, Bangerth W. (2016) WorkStream -- A Design Pattern for Multicore-Enabled Finite Element Computations Acm Transactions On Mathematical Software. 43: 2
Frohne J, Heister T, Bangerth W. (2016) Efficient numerical methods for the large-scale, parallel solution of elastoplastic contact problems International Journal For Numerical Methods in Engineering. 105: 416-439
Aguilar O, Allmaras M, Bangerth W, et al. (2015) Statistics of parameter estimates: A concrete example Siam Review. 57: 131-149
Chueh CC, Djilali N, Bangerth W. (2013) An h-adaptive operator splitting method for two-phase flow in 3D heterogeneous porous media Siam Journal On Scientific Computing. 35
Allmaras M, Bangerth W, Linhart JM, et al. (2013) Estimating parameters in physical models through bayesian inversion: A complete example Siam Review. 55: 149-167
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