Queens

Publications

Journal publications in which QUEENS has been used

Journal Articles

  1. Dinkel, M., Geitner, C. M., Rei, G. R., Nitzler, J., & Wall, W. A. (2024). Solving Bayesian inverse problems with expensive likelihoods using constrained Gaussian processes and active learning. Inverse Problems, 40(9), 095008. doi:10.1088/1361-6420/ad5eb4
  2. Hervas-Raluy, S., Wirthl, B., Guerrero, P. E., Robalo Rei, G., Nitzler, J., Coronado, E., de Mora Sainz, J. F., Schrefler, B. A., Gomez-Benito, M. J., García-Aznar, J. M., & Wall, W. A. (2023). Tumour Growth: An Approach to Calibrate Parameters of a Multiphase Porous Media Model Based on in Vitro Observations of Neuroblastoma Spheroid Growth in a Hydrogel Microenvironment. Computers in Biology and Medicine, 159, 106895. doi:10.1016/j.compbiomed.2023.106895
  3. Wirthl, B., Brandstaeter, S., Nitzler, J., Schrefler, B. A., & Wall, W. A. (2023). Global Sensitivity Analysis Based on Gaussian-process Metamodelling for Complex Biomechanical Problems. International Journal for Numerical Methods in Biomedical Engineering, 39(3), e3675. doi:10.1002/cnm.3675
  4. Nitzler, J., Biehler, J., Fehn, N., Koutsourelakis, P.-S., & Wall, W. A. (2022). A Generalized Probabilistic Learning Approach for Multi-Fidelity Uncertainty Quantification in Complex Physical Simulations. Computer Methods in Applied Mechanics and Engineering, 400, 115600. doi:10.1016/j.cma.2022.115600
  5. Willmann, H., & Wall, W. A. (2022). Inverse Analysis of Material Parameters in Coupled Multi-Physics Biofilm Models. Advanced Modeling and Simulation in Engineering Sciences, 9(1), 7. doi:10.1186/s40323-022-00220-0
  6. Willmann, H., Nitzler, J., Brandstäter, S., & Wall, W. A. (2022). Bayesian Calibration of Coupled Computational Mechanics Models under Uncertainty Based on Interface Deformation. Advanced Modeling and Simulation in Engineering Sciences, 9(1), 24. doi:10.1186/s40323-022-00237-5
  7. Meier, C., Fuchs, S. L., Much, N., Nitzler, J., Penny, R. W., Praegla, P. M., Proell, S. D., Sun, Y., Weissbach, R., Schreter, M., Hodge, N. E., John Hart, A., & Wall, W. A. (2021). Physics-Based Modeling and Predictive Simulation of Powder Bed Fusion Additive Manufacturing across Length Scales. GAMM-Mitteilungen, 44(3), e202100014. doi:10.1002/gamm.202100014
  8. Nitzler, J., Meier, C., Müller, K. W., Wall, W. A., & Hodge, N. E. (2021). A novel physics-based and data-supported microstructure model for part-scale simulation of laser powder bed fusion of Ti-6Al-4V. Advanced Modeling and Simulation in Engineering Sciences, 8(1), 16. doi:10.1186/s40323-021-00201-9
  9. Brandstaeter, S., Fuchs, S. L., Biehler, J., Aydin, R. C., Wall, W. A., & Cyron, C. J. (2021). Global Sensitivity Analysis of a Homogenized Constrained Mixture Model of Arterial Growth and Remodeling. Journal of Elasticity. doi:10.1007/s10659-021-09833-9
  10. Pivovarov, D., Willner, K., Steinmann, P., Brumme, S., Müller, M., Srisupattarawanit, T., Ostermeyer, G.-P., Henning, C., Ricken, T., Kastian, S., Reese, S., Moser, D., Grasedyck, L., Biehler, J., Pfaller, M., Wall, W., Kohlsche, T., von Estorff, O., Gruhlke, R., … Leyendecker, S. (2019). Challenges of Order Reduction Techniques for Problems Involving Polymorphic Uncertainty. GAMM-Mitteilungen, 42(2), e201900011. doi:10.1002/gamm.201900011

PhD Theses

  1. Brandstäter, S. (2021). Global Sensitivity Analysis for Models of Active Biomechanical Systems [Technische Universität München]. Link

Queens