Buchbeiträge
Zeige Ergebnisse 1 - 11 von 11
2025
Salomon, J., Broggi, M., & Beer, M. (2025). Resilience-Based Decision Criteria for Optimal Regeneration. In Regeneration of Complex Capital Goods: Contributions to the Final Symposium of the Collaborative Research Center 871 (S. 393-422) https://doi.org/10.1007/978-3-031-51395-4_20
2024
Tyrsin, A. N., Kashcheev, S. E., Beer, M., Kashcheev, S. E., & Gerget, O. M. (2024). Entropy Indicators of Cascading Failures Risk in Gaussian Interconnected Network Structures. In Cyber-Physical Systems: Industry 4.0 to Industry 5.0 Transition (S. 219-234). (Studies in Systems, Decision and Control; Band 560). https://doi.org/10.1007/978-3-031-67911-7_17
2023
Beer, M. (2023). Fuzzy Probability Theory. In Granular, Fuzzy, and Soft ComputingTsau: A Volume in the Encyclopedia ofComplexity and Systems Science,Second Edition (S. 51–75). (Encyclopedia of Complexity and Systems Science Series). https://doi.org/10.1007/978-1-0716-2628-3_237
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; Band 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 (S. 251-256). (Studies in Systems, Decision and Control; Band 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 (S. 21-48). (Springer Series in Reliability Engineering; Band Part F266). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-28859-3_2
Wei, P., & Beer, M. (2023). Regression Models for Machine Learning. In Machine Learning in Modeling and Simulation : Methods and Applications (Computational Methods in Engineering & the Sciences (CMES) ). https://doi.org/10.1007/978-3-031-36644-4_9
2022
Bi, S., & Beer, M. (2022). Overview of Stochastic Model Updating in Aerospace Application Under Uncertainty Treatment. In Uncertainty in Engineering. SpringerBriefs in Statistics (S. 115-130). (Uncertainty in Engineering. SpringerBriefs in Statistics (BRIEFSSTATIST)). https://doi.org/10.1007/978-3-030-83640-5_8
2017
Neumann, I., Beer, M., Gong, Z., Sriboonchitta, S., & Kreinovich, V. (2017). What if we do not know correlations? In Studies in Computational Intelligence (S. 78-85). (Studies in Computational Intelligence; Band 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 (Hrsg.), Computational Complexity: Theory, Techniques, and Applications (S. 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 (S. 2074-2077). Elsevier Inc.. https://doi.org/10.1016/B978-008044046-0.50509-1
Journal-Artikel
Zeige Ergebnisse 1 - 20 von 319
2025
Dai, H., Li, D., & Beer, M. (2025). Adaptive Kriging-assisted multi-fidelity subset simulation for reliability analysis. Computer Methods in Applied Mechanics and Engineering, 436, Artikel 117705. https://doi.org/10.1016/j.cma.2024.117705
Grashorn, J., Bittner, M., Banse, M., Chang, X., Beer, M., & Fau, A. (2025). Namazu: Low-Cost Tunable Shaking Table for Vibration Experiments Under Generic Signals. Experimental techniques, 49, 97–115. https://doi.org/10.1007/s40799-024-00727-8
Hong, F., Song, J., Wei, P., Huang, Z., & Beer, M. (2025). A stratified beta-sphere sampling method combined with important sampling and active learning for rare event analysis. Structural safety, 112, Artikel 102546. https://doi.org/10.1016/j.strusafe.2024.102546
Hong, F., Wei, P., Bi, S., & Beer, M. (2025). Efficient variational Bayesian model updating by Bayesian active learning. Mechanical Systems and Signal Processing, 224, Artikel 112113. https://doi.org/10.1016/j.ymssp.2024.112113
Lai, J., Wang, K., Shi, Y., Xu, J., Chen, J., Wang, P., & Beer, M. (2025). Reliability assessment of freight wagon passing through railway turnouts using adaptive Kriging surrogate model. International Journal of Rail Transportation, 13(1), 49-68. https://doi.org/10.1080/23248378.2024.2304000
Lei, Z., Tian, F., Su, Y., Wen, G., Feng, K., Chen, X., Beer, M., & Yang, C. (2025). Unsupervised graph transfer network with hybrid attention mechanism for fault diagnosis under variable operating conditions. Reliability Engineering and System Safety, 255, Artikel 110684. https://doi.org/10.1016/j.ress.2024.110684
Li, Y., Geng, C., Yang, Y., Chen, S., Feng, K., & Beer, M. (2025). Extraction of instantaneous frequencies for signals with intersecting and intermittent trajectories. Mechanical Systems and Signal Processing, 223, Artikel 111835. https://doi.org/10.1016/j.ymssp.2024.111835
Liao, K., Zhao, X., Wu, Y., Miao, F., Pan, Y., & Beer, M. (2025). Efficient reliability analysis of slopes in spatially variable soils with active learning-assisted bootstrap polynomial chaos expansion. Computers and geotechnics, 179, Artikel 107022. https://doi.org/10.1016/j.compgeo.2024.107022
Liu, J., Shi, Y., Ding, C., & Beer, M. (2025). Efficient global sensitivity analysis framework and approach for structures with hybrid uncertainties. Computer Methods in Applied Mechanics and Engineering, 436, Artikel 117726. https://doi.org/10.1016/j.cma.2024.117726
Luo, Y., Dang, C., Broggi, M., & Beer, M. (2025). Stochastic dynamic response analysis via dimension-reduced probability density evolution equation (DR-PDEE) with enhanced tail-accuracy. Probabilistic Engineering Mechanics, 79, Artikel 103735. https://doi.org/10.1016/j.probengmech.2025.103735
Mei, L. F., Yan, W. J., Yuen, K. V., & Beer, M. (2025). Streaming variational inference-empowered Bayesian nonparametric clustering for online structural damage detection with transmissibility function. Mechanical Systems and Signal Processing, 222, Artikel 111767. https://doi.org/10.1016/j.ymssp.2024.111767
Mo, J., Yan, W. J., Yuen, K. V., & Beer, M. (2025). Efficient non-probabilistic parallel model updating based on analytical correlation propagation formula and derivative-aware deep neural network metamodel. Computer Methods in Applied Mechanics and Engineering, 433(Part A), Artikel 117490. https://doi.org/10.1016/j.cma.2024.117490
Song, J., Liang, Z., Wei, P., & Beer, M. (2025). Sampling-based adaptive Bayesian quadrature for probabilistic model updating. Computer Methods in Applied Mechanics and Engineering, 433(Part A), Artikel 117467. https://doi.org/10.1016/j.cma.2024.117467
Wang, R., Chen, G., Liu, Y., & Beer, M. (2025). Seismic Reliability Assessment Framework for Unsaturated Soil Slope under Near-Fault Pulse-Like Ground Motion. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 11(2), Artikel 04025005. Vorabveröffentlichung online. https://doi.org/10.1061/AJRUA6.RUENG-1227
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, Artikel 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, Artikel 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), Artikel 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, Artikel 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, Artikel 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
