Book Chapter
Showing results 1 - 6 out of 6
2023
Chen, Y., Patelli, E., Edwards, B., & Beer, M. (2023). Spectral Density Estimation Of Stochastic Processes Under Missing Data And Uncertainty Quantification With Bayesian Deep Learning. In Ecomas Proceedia UNCECOMP 2023 (International Conference on Uncertainty Quantification in Computational Science and Engineering; Vol. 5). National Technical University of Athens. https://doi.org/10.7712/120223.10371.19949
Galindo, O., Ibarra, C., Kreinovich, V., & Beer, M. (2023). Fourier Transform and Other Quadratic Problems Under Interval Uncertainty. In Decision Making Under Uncertainty and Constraints (pp. 251-256). (Studies in Systems, Decision and Control; Vol. 217). Springer Verlag. https://doi.org/10.1007/978-3-031-16415-6_37
Jerez, D. J., Jensen, H. A., & Beer, M. (2023). A Two-Phase Sampling Approach for Reliability-Based Optimization in Structural Engineering. In Advances in Reliability and Maintainability Methods and Engineering Applications: Essays in Honor of Professor Hong-Zhong Huang on his 60th Birthday (pp. 21-48). (Springer Series in Reliability Engineering; Vol. Part F266). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-28859-3_2
2017
Neumann, I., Beer, M., Gong, Z., Sriboonchitta, S., & Kreinovich, V. (2017). What if we do not know correlations? In Studies in Computational Intelligence (pp. 78-85). (Studies in Computational Intelligence; Vol. 760). Springer Verlag. https://doi.org/10.1007/978-3-319-73150-6_5
2012
Beer, M. (2012). Fuzzy probability theory. In R. A. Meyers (Ed.), Computational Complexity: Theory, Techniques, and Applications (pp. 1240-1252). Springer New York. https://doi.org/10.1007/978-1-4614-1800-9_76
2003
Möller, B., Graf, W., Beer, M., & Sickert, J. U. (2003). Fuzzy stochastic finite element method. In Computational Fluid and Solid Mechanics 2003 (pp. 2074-2077). Elsevier Inc.. https://doi.org/10.1016/B978-008044046-0.50509-1
Journal Articles
Showing results 1 - 20 out of 307
2024
Behrendt, M., Dang, C., & Beer, M. (2024). Data-driven and physics-based interval modelling of power spectral density functions from limited data. Mechanical Systems and Signal Processing, 208, Article 111078. https://doi.org/10.1016/j.ymssp.2023.111078
Behrendt, M., Lyu, M. Z., Luo, Y., Chen, J. B., & Beer, M. (2024). Failure probability estimation of dynamic systems employing relaxed power spectral density functions with dependent frequency modeling and sampling. Probabilistic Engineering Mechanics, 75, Article 103592. https://doi.org/10.1016/j.probengmech.2024.103592
Behrensdorf, J., Broggi, M., & Beer, M. (2024). Interval Predictor Model for the Survival Signature Using Monotone Radial Basis Functions. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 10(3), Article 04024034. https://doi.org/10.1061/AJRUA6.RUENG-1219
Bittner, M., Broggi, M., & Beer, M. (2024). Efficient reliability analysis of stochastic dynamic first-passage problems by probability density evolution analysis with subset supported point selection. Engineering structures, 312, Article 118210. https://doi.org/10.1016/j.engstruct.2024.118210
Bittner, M., Behrendt, M., & Beer, M. (2024). Relaxed evolutionary power spectral density functions: A probabilistic approach to model uncertainties of non-stationary stochastic signals. Mechanical Systems and Signal Processing, 211, Article 111210. https://doi.org/10.1016/j.ymssp.2024.111210
Brandt, J., Iversen, T., Eckert, C., Peterssen, F., Bensmann, B., Bensmann, A., Beer, M., Weyer, H., & Hanke-Rauschenbach, R. (2024). Cost and competitiveness of green hydrogen and the effects of the European Union regulatory framework. Nature energy, 9, 703–713. https://doi.org/10.1038/s41560-024-01511-z
Chen, G., Yang, J., Wang, R., Li, K., Liu, Y., & Beer, M. (2024). Response to discussion of “Seismic damage analysis due to near-fault multipulse ground motion”. Earthquake Engineering and Structural Dynamics, 53(2), 861-866. https://doi.org/10.1002/eqe.4046
Dang, C., Valdebenito, M. A., Wei, P., Song, J., & Beer, M. (2024). Bayesian active learning line sampling with log-normal process for rare-event probability estimation. Reliability Engineering and System Safety, 246, Article 110053. https://doi.org/10.1016/j.ress.2024.110053
Dang, C., Faes, M. G. R., Valdebenito, M. A., Wei, P., & Beer, M. (2024). Partially Bayesian active learning cubature for structural reliability analysis with extremely small failure probabilities. Computer Methods in Applied Mechanics and Engineering, 422, Article 116828. https://doi.org/10.1016/j.cma.2024.116828
Dang, C., & Beer, M. (2024). Semi-Bayesian active learning quadrature for estimating extremely low failure probabilities. Reliability Engineering and System Safety, 246, Article 110052. https://doi.org/10.1016/j.ress.2024.110052
Dang, C., Cicirello, A., Valdebenito, M. A., Faes, M. G. R., Wei, P., & Beer, M. (2024). Structural reliability analysis with extremely small failure probabilities: A quasi-Bayesian active learning method. Probabilistic Engineering Mechanics, 76, Article 103613. https://doi.org/10.1016/j.probengmech.2024.103613
Ding, C., Dang, C., Broggi, M., & Beer, M. (2024). Estimation of Response Expectation Bounds under Parametric P-Boxes by Combining Bayesian Global Optimization with Unscented Transform. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 10(2), Article 04024017. https://doi.org/10.1061/AJRUA6.RUENG-1169
Ding, C., Wei, P., Shi, Y., Liu, J., Broggi, M., & Beer, M. (2024). Sampling and active learning methods for network reliability estimation using K-terminal spanning tree. Reliability Engineering and System Safety, 250, Article 110309. https://doi.org/10.1016/j.ress.2024.110309
Elias, S., & Beer, M. (2024). Vibration control and energy harvesting of offshore wind turbines installed with TMDI under dynamical loading. Engineering structures, 315, Article 118459. https://doi.org/10.1016/j.engstruct.2024.118459
Feng, C., Valdebenito, M. A., Chwała, M., Liao, K., Broggi, M., & Beer, M. (2024). Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments. Journal of Rock Mechanics and Geotechnical Engineering, 16(4), 1140-1152. https://doi.org/10.1016/j.jrmge.2023.09.006
Grashorn, J., Broggi, M., Chamoin, L., & Beer, M. (2024). Efficiency comparison of MCMC and Transport Map Bayesian posterior estimation for structural health monitoring. Mechanical Systems and Signal Processing, 216, Article 111440. https://doi.org/10.1016/j.ymssp.2024.111440
Grashorn, J., Bittner, M., Banse, M., Chang, X., Beer, M., & Fau, A. (2024). Namazu: Low-Cost Tunable Shaking Table for Vibration Experiments Under Generic Signals. Experimental techniques. Advance online publication. https://doi.org/10.1007/s40799-024-00727-8
Hong, F., Wei, P., Fu, J., & Beer, M. (2024). A sequential sampling-based Bayesian numerical method for reliability-based design optimization. Reliability Engineering and System Safety, 244, Article 109939. https://doi.org/10.1016/j.ress.2024.109939
Hong, F., Wei, P., & Beer, M. (2024). Parallelization of adaptive Bayesian cubature using multimodal optimization algorithms. Engineering Computations (Swansea, Wales), 41(2), 413-437. https://doi.org/10.1108/EC-12-2023-0957
Hu, Z., Dang, C., Wang, L., & Beer, M. (2024). Parallel Bayesian probabilistic integration for structural reliability analysis with small failure probabilities. Structural safety, 106, Article 102409. https://doi.org/10.1016/j.strusafe.2023.102409
