Publications funded or partly funded by Helmholtz-UQ.

  • Albaraghtheh, T., Willumeit-Römer, R., Zeller-Plumhoff, B. (2022):
    In silico studies of magnesium-based implants: A review of the current stage and challenges
    Journal of Magnesium and Alloys
    DOI: 10.1016/j.jma.2022.09.029
  • AlBaraghtheh, T., Hermann, A., Shojaei, A., Willumeit-Römer, R., Cyron, C. J., & Zeller-Plumhoff, B. (2023):
    Utilizing Computational Modelling to Bridge the Gap between In Vivo and In Vitro Degradation Rates for Mg-xGd Implants
    Corrosion and Materials Degradation, 4(2), 274-283
    DOI: 10.3390/cmd4020014
  • Albaraghtheh, T., Willumeit-Römer, R., Zeller-Plumhoff, B. (2024).
    Best practices in developing a UQ-workflow for modelling the biodegradation of Mg-based implants.
    Under review in Advanced Science.
  • Amrhein L, Fuchs C (2020):
    Stochastic profiling of mRNA counts using HMC
    In: Proceedings of the 35th International Workshop on Statistical Modelling (IWSM) 2020.
  • Amrhein L, Fuchs C (2021):
    stochprofML: Stochastic Profiling Using Maximum Likelihood Estimation in R
    BMC Bioinformatics 22: 123.
    DOI: 10.1186/s12859-021-03970-7
  • Böttcher, P. C., Witthaut, D., & Rydin Gorjão, L. (2022).
    Dynamic stability of electric power grids: Tracking the interplay of the network structure, transmission losses, and voltage dynamics.
    Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(5).
  • Böttcher, P. C., Gorjão, L. R., & Witthaut, D. (2023).
    Stability bounds of droop-controlled inverters in power grid networks.
    IEEE Access.
  • Böttcher, P. C., Gorjão, L. R., Beck, C., Jumar, R., Maass, H., Hagenmeyer, V., Witthaut, D., & Schäfer, B. (2023).
    Initial analysis of the impact of the Ukrainian power grid synchronization with Continental Europe.
    Energy Advances, 2(1), 91-97.
  • Böttcher, P. C., Schäfer, B., Kettemann, S., Agert, C., & Witthaut, D. (2023).
    Local versus global stability in dynamical systems with consecutive Hopf bifurcations.
    Physical Review Research, 5(3), 033139.
  • Contento L, Castelletti N, Raimúndez E, Le Gleut R, Schälte Y, Stapor P, Hinske LC, Hoelscher M, Wieser A, Radon K, Fuchs C, et al. (2021):
    Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infection rates
    medRxiv.
    DOI: 10.1101/2021.10.01.21263052
  • Cramer, E., Gorjão, L. R., Mitsos, A., Schäfer, B., Witthaut, D., & Dahmen, M. (2022).
    Validation Methods for Energy Time Series Scenarios from Deep Generative Models.
    IEEE Access, 10, 8194
  • Fuetterer C, Augustin T, Fuchs C (2020):
    Adapted single-cell consensus clustering (adaSC3)
    Advances in Data Analysis and Classification volume 14(4): 885–896.
    DOI: 10.1007/s11634-020-00428-1
  • Gault, J. A., Freund, J. A., Hillebrand, H., & Gross, T. (2023).
    Dissimilarity analysis based on diffusion maps.
    Oikos, e10249. DOI:doi.org/10.1111/oik.10249
  • S. Germscheid, M. Dahmen, A. Mitsos (2021).
    Assessing the Demand Response Potential of Power-Intensive Processes by Stochastic Scheduling Optimization.
    AIChE Annual Meeting, Nov 7-19, Boston, USA (2021)
  • Germscheid, S. H., Mitsos, A., & Dahmen, M. (2022)
    Demand Response Potential of Industrial Processes Considering Uncertain Short‐term Electricity Prices.
    AIChE Journal, e17828.
    DOI:10.1002/aic.17828
  • Germscheid, S. H., Röben, F. T., Sun, H., Bardow, A., Mitsos, A., & Dahmen, M. (2023).
    Demand response scheduling of copper production under short-term electricity price uncertainty.
    Computers & Chemical Engineering, 178, 108394.
  • Germscheid, S. H., Mitsos, A., & Dahmen, M. (2024)
    Scenario Reduction Methods for Risk-Averse Demand Response Scheduling under Price Uncertainty.
    Proceedings of the 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering (ESCAPE34/ PSE24), Florence, Italy.
  • Germscheid, S. H., Nilges, B., von der Assen, N., Mitsos, A., & Dahmen, M. (2024).
    Optimal design of a local renewable electricity supply system for power-intensive production processes with demand response.
    Computers & Chemical Engineering, 185, 108656.
  • Gorjão, L. R., Jumar, R., Maass, H., Hagenmeyer, V., Yalcin, G. C., Kruse, J., Timme, M., Beck, C., Witthaut, D., & Schäfer, B. (2020).
    Open database analysis of scaling and spatio-temporal properties of power grid frequencies.
    Nature communications, 11(1), 6362.
  • Gorjão, L. R., Schäfer, B., Witthaut, D., & Beck, C. (2021).
    Spatio-temporal complexity of power-grid frequency fluctuations.
    New Journal of Physics, 23, 073016.
  • Gorjão, L. R., Witthaut, D., Lehnertz, K., & Lind, P. G. (2021).
    Arbitrary-Order Finite-Time Corrections for the Kramers–Moyal Operator.
    Entropy, 23(5), 517.
  • Gorjão, L. R., Vanfretti, L., Witthaut, D., Beck, C., & Schäfer, B. (2021).
    Phase and amplitude synchronisation in power-grid frequency fluctuations in the Nordic Grid.
    IEEE Access, 10, 18065–18073.
  • Gorjão, L. R., Hassan, G., Kurths, J., & Witthaut, D. (2022).
    MFDFA: Efficient Multifractal Detrended Fluctuation Analysis in Python.
    Computer Physics Communications, 273, 108254.
  • Gorjão, L. R., Witthaut, D., & Lind, P. G. (2023).
    jumpdiff: A Python library for statistical inference of jump-diffusion processes in observational or experimental data sets.
    Journal of Statistical Software, 105, 1-22.
  • Graham, Jasmin, Angelos Hannides, Nabir Mamnun, Lina E. Sitz, Ian D. Walsh, Elisha M. Wood-Charlson, and Leandro Ponsoni.
    “Ocean Sciences Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science.”
    Earth and Space Science 9 (2022), e2021EA002124.
    DOI: 10.1029/2021EA002124
  • Han, C., Hilger, H., Mix, E., Böttcher, P., Reyers, M., Beck, C., Witthaut, D., & Gorjão, L. R. (2022).
    Complexity and persistence of price time series of the European electricity spot market.
    PRX Energy, 1(1), 013002.
  • Han, C., Witthaut, D., Gorjao, L. R., & Böttcher, P. C. (2022).
    Collective effects and synchronization of demand in real-time demand response.
    Journal of Physics: Complexity, 3(2), 025002.
  • Han, C., Schröder, M., Witthaut, D., & Böttcher, P. C. (2023).
    Formation of trade networks by economies of scale and product differentiation.
    Journal of Physics: Complexity, 4(2), 025006.
  • Illien, L., Sens-Schönfelder, C., & Ke, K. Y. (2023).
    Resolving minute temporal seismic velocity changes induced by earthquake damage: the more stations, the merrier?.
    Geophysical Journal International, 234(1), 124-135.
  • A. Jozi Najafabadi, Ch. Haberland, T. Ryberg, V. Verwater, E. Le Breton, M. R. Handy, M. Weber, and the AlpArray working group (2021):
    Relocation of earthquakes in the Southern and Eastern Alps (Austria, Italy) recorded by the dense, temporary SWATH–D network using a Markov chain Monte Carlo inversion.
    Solid Earth, 12, 5, 1087-1109.
    DOI: 10.5194/se-12-1087-2021
  • Jumar, R., Maaß, H., Schäfer, B., Gorjão, L. R., & Hagenmeyer, V. (2020).
    Power grid frequency data base.
    arXiv preprint arXiv:2006.01771.
  • Kaiser, F., Böttcher, P. C., Ronellenfitsch, H., Latora, V., & Witthaut, D. (2022).
    Dual communities in spatial networks.
    Nature Communications, 13(1), 7479.
  • Kästle, E. D., Tilmann, F., & AlpArray and Swath‐D Working Groups. (2024).
    Anisotropic Reversible‐Jump McMC Shear‐Velocity Tomography of the Eastern Alpine Crust.
    Geochemistry, Geophysics, Geosystems, 25(3), e2023GC011238.
  • Ke, K.-Y., Tilmann, F., Ryberg, T., Dreiling, J. (2024)
    Uncertainty Quantification in Radial Anisotropy Models Based on Transdimensional Bayesian Inversion of Receiver Functions and Surface Wave Dispersion: Case Study Sri Lanka
    Accepted in Bulletin of the Seismological Society of America
  • Mamnun, Nabir, Christoph Völker, Mihalis Vrekoussis, and Lars Nerger (2022).
    Uncertainties in Ocean Biogeochemical Simulations: Application of Ensemble Data Assimilation to a One-Dimensional Model.
    Original Research, Frontiers in Marine Science 9.
    DOI: 10.3389/fmars.2022.984236
  • Mamnun, N., Völker, C., Krumscheid, S., Vrekoussis, M., & Nerger, L. (2023).
    Global sensitivity analysis of a one-dimensional ocean biogeochemical model.
    Socio-Environmental Systems Modelling, 5, 18613.
    DOI: 10.18174/sesmo.18613
  • Min, C., Yang, Q., Luo, H., Chen, D., Krumpen, T., Mamnun, N., … & Nerger, L. (2023).
    Improving Arctic sea-ice thickness estimates with the assimilation of CryoSat-2 summer observations.
    Ocean-Land-Atmosphere Research, 2, 0025.
    DOI: 10.34133/olar.0025
  • Olbrich L, Castelletti N, Schälte Y, Garí M, Pütz P, Bakuli A, Pritsch M, Kroidl I, Saathoff E, Noller JMG, Fingerle V, Fuchs C, et al. (2021):
    A Serology Strategy for Epidemiological Studies Based on the Comparison of the Performance of Seven Different Test Systems - The Representative COVID-19 Cohort Munich
    bioRxiv.
    DOI: 10.1101/2021.01.13.21249735
  • Olbrich L, Castelletti N, Schälte Y, Garí M, Pütz P, Bakuli A, Pritsch M, Kroidl I, Saathoff E, Guggenbuehl Noller J, Fingerle V, Fuchs C, et al. (2021):
    Head-to-head evaluation of seven different seroassays including direct viral neutralisation in a representative cohort for SARS-CoV-2
    Journal of General Virology 102(10).
    DOI: 10.1099/jgv.0.001653
  • Paasche, H., Paasche, K., Dietrich, P., (2020):
    Uncertainty as a driving force for geoscientific development
    Nat. Cult. 15 (1), 1 - 18
    DOI: 10.3167/nc.2020.150101
  • Paasche, H., Gross, M., Lüttgau, J., Greenberg, D.S., Weigel, T., (2021):
    To the brave scientists: Aren’t we strong enough to stand (and profit from) uncertainty in Earth system measurement and modelling?
    Geosci. Data J.
    DOI: 10.1002/gdj3.132
  • Paasche, H., Bleicher, A., Loh, W., & Weigel, T. (2022).
    Ignorance of Model Uncertainty and its Effects on Ethics and Society Using the Example of Geosciences.
    In Routledge International Handbook of Ignorance Studies (pp. 118-126). Routledge.
  • Pieschner S, Fuchs C (2020):
    Bayesian inference for diffusion processes: using higher-order approximations for transition densities
    Royal Society Open Science 7(10): 200270.
    DOI: 10.1098/rsos.200270
  • Pieschner S, Hasenauer J, Fuchs C (2021):
    Identifiability analysis for models of the translation kinetics after mRNA transfection
    bioRxiv (accepted by Journal of Mathematical Biology).
    DOI: 10.1101/2021.05.18.444633
  • Pritsch M, Radon K, Bakuli A, Le Gleut R, Olbrich L, Guggenbuehl Noller JM, Saathoff E, Castelletti N, Garí M, Pütz P, Schaelte Y, Fuchs C, et al. (2021):
    Prevalence and Risk Factors of Infection in the Representative COVID-19 Cohort Munich
    International journal of environmental research and public health 18(7): 3572.
    DOI: 10.2139/ssrn.3745128
  • Radon K, Bakuli A, Pütz P, Gleut RL, Guggenbuehl Noller JM, Olbrich L, Saathoff E, Garí M, Schälte Y, Frahnow T, Wölfel R, Fuchs C, et al. (2021):
    From first to second wave: follow-up of the prospective Covid-19 cohort (KoCo19) in Munich (Germany)
    BMC infectious diseases 21(1): 925.
    DOI: 10.1186/s12879-021-06589-4
  • Riechers, K., Rydin Gorjão, L., Hassanibesheli, F., Lind, P. G., Witthaut, D., & Boers, N. (2023).
    Stable stadial and interstadial states of the last glacial’s climate identified in a combined stable water isotope and dust record from Greenland.
    Earth System Dynamics, 14(3), 593-607.
  • Tilmann, F., Sadeghisorkhani, H., Mauerberger, A. (2020):
    Another look at the treatment of data uncertainty in Markov chain Monte Carlo inversion and other probabilistic methods.
    Geophysical Journal International, 222, 1, 388-405.
    DOI: 10.1093/gji/ggaa168
  • Trebbien, J., Pütz, S., Schäfer, B., Nygård, H. S., Gorjão, L. R., & Witthaut, D. (2023, October).
    Probabilistic forecasting of day-ahead electricity prices and their volatility with LSTMs.
    In 2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE) (pp. 1-5). IEEE.
  • Trebbien, J., Gorjão, L. R., Praktiknjo, A., Schäfer, B., & Witthaut, D. (2023).
    Understanding electricity prices beyond the merit order principle using explainable AI.
    Energy and AI, 13, 100250.
  • B. Zeller-Plumhoff*ǂ, T. AlBaraghthehǂ, D. Höche, R. Willumeit-Römer
    Computational modelling of magnesium degradation in simulated body fluid under physiological conditions
    Accepted at Magnesium and Alloys
    ǂ shared first authorship
  • Zöller, G., Hainzl, S., Tilmann, F., Woith, H., Dahm, T. (2021):
    Comment on “Potential short‐term earthquake forecasting by farm animal monitoring” by Wikelski, Mueller, Scocco, Catorci, Desinov, Belyaev, Keim, Pohlmeier, Fechteler, and Mai.
    Ethology, 127, 3, 302-306.
    DOI: 10.1111/eth.13105