REC 2026 - 11th International Workshop on Reliable Engineering Computing: Reliability Computations in a Data and Model-Driven World
Schlagworte:
Reliable engineering computing, Uncertainty quantification, Reliability analysis, Imprecise probability, Interval methods, Scientific machine learning, Bayesian inference, Surrogate modelling, Random fields, Stochastic dynamics, Rare event simulation, Reliability-based design optimizationÜber dieses Buch
The International Workshop on Reliable Engineering Computing (REC) has served as a distinctive multidisciplinary forum for more than two decades, bringing together researchers from engineering, mathematics, computer science and statistics with the shared objective of advancing the reliability of engineering computations. This volume collects the contributions to the 11th edition, REC 2026, hosted by TU Dortmund University under the theme Reliability Computations in a Data and Model-Driven World.
The proceedings comprise 51 peer-reviewed papers organised into eight thematic sessions: artificial intelligence, explainability and scientific machine learning; reliability analysis and sensitivity methods; hybrid, imprecise and set-based uncertainty; spatial and temporal uncertainties; Bayesian inference, active learning and surrogate modelling; engineering applications and monitoring; stochastic dynamics and computational methods; and rare events, reliability and optimization. Abstracts of four keynote and two junior-keynote lectures are also included.
Together, the contributions address a central question: how can data-driven methods and machine learning be integrated with established engineering principles and physical modelling so that computational predictions remain transparent, trustworthy and scientifically sound? Applications range from structural and mechanical systems to infrastructure, materials, robotics and environmental problems, reflecting the increasingly interdisciplinary nature of modern research on reliability and uncertainty quantification.
Kapitel
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Session 1 - Artificial intelligence, explainability and scientific machine learning
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How can we make reliable engineering computing explainable
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Data and prior knowledge driven FMEA severity assessment via deep attention networks
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Can large language models find the design point? Benchmarking LLM-based MPP search in structural reliability
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Adaptive kernel operator learning for spatiotemporal full-field reliability analysis with mixed random inputs
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Quantification of modeling uncertainty in physics-informed neural networks by interval arithmetic
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Trustworthy direct interval propagation via conformal prediction in neural network surrogates
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Application of machine learning to reduce model uncertainty and enable resource-efficient design of flat slabs
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Session 2 - Reliability anlaysis and sensitivity methods
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Beyond the design pointwhen equal failure probabilities may not be equal
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Hybrid reliability analysis with uncertainty in correlation coefficients and first two statistical moments
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An efficient reliability sensitivity analysis method based on the Method of Moments
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A linear-moment-based sensitivity analysis method considering the influence of correlation coefficient
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An efficient reliability analysis method under hybrid uncertainty for high-dimensional problems
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A bivariate distribution based on squared normal transformation
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Session 3 - Hybrid, imprecise and set-based uncertainty
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A hybrid interval uncertainty algorithm for structural analysis
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A practical framework for failure probability assessment under aleatory and epistemic uncertainty
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Possibilistic filtering for reliable robot localization
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Possibilistic uncertainty modeling with hyperellipsoids
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Imprecise probabilistic subset simulation for rigorous bounding of the failure probability
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Session 4 - Spatial and temporal uncertainties
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Microstructure compatible random field models of heterogeneous materials
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A transformation-based framework for modeling and simulation of multivariate non-Gaussian random fields
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Fuzzy fields based on the parallelepiped dependency model
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Surrogate modeling strategy for random fields with uncertain correlation lengths
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Random field modeling approach for uncertainty quantification of reused concrete elements
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Three-dimensional multivariable non-Gaussian random field modeling and simulation of concrete constitutive law
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Spectrally consistent upscaling of wind speed time series data
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Session 5 - Bayesian inference, active learning and surrogates
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Probability-box propagation with interval prediction models: a reproducible Python workflow
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Multivariate active learning for polynomial chaos expansion
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Probabilistic fatigue life modelling of additively manufactured aluminum alloys via Bayesian inference
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Combining Bayesian active learning and stratified sampling for bounding small failure probability under hybrid uncertainties
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PINN-accelerated diffusion models for amortized Bayesian inversion of chloride transport in concrete
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Bayesian calibration of resistance spot welding models using temporal process features and streamlined active learning
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Session 6 - Engineering applications and monitoring
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Stability analysis of concrete gravity dams under uncertainty
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Probabilistic assessment of multiphysics-driven stochastic corrosion for the safety evaluation of nuclear waste canisters
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Probabilistic reliability evaluation of autonomous navigation for firefighting robots in uncertain environments
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Integrating deterministic and stochastic uncertainty in terrestrial laser scanner-based deformation monitoring
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A holistic approach to the Life Cycle Assessment of a propeller system
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Session 7 - Stochastic dynamics and computational methods
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A generalized autoregressive model for time-variant dynamical systems
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Stochastic contact analysis and its applications
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A continuous-time Markovian framework for performance-based earthquake engineeringintegrated seismic risk and resilience assessment
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Neural network-driven uncertainty qualification analysis of printed circuit heat exchangers
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Towards a set-based IVP solver for ODEs on the GPU
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Efficient probabilistic response analysis of large-scale engineering structures under stochastic excitation via the DR-PDEE incorporating an auxiliary diffusion process
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A regression-tree-based method for structural reliability assessment
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Probabilistic resilience analysis of urban road network considering wind damage to street trees via dimension-reduced probability density evolution equation (DR-PDEE)
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Session 8 - Rare events, reliability and optimization
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A computationally efficient framework for the propagation of interval-process uncertainty
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A practical stratified importance sampling strategy for extremely rare event estimation in high dimensional systems
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Uncertainty analysis of fatigue failure using a fuzzy approach
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A weighted extreme value moment method using adaptive sparse grids for time-variant reliability-based design optimization
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Efficient robust topology optimization of large-scale continuum structures under load uncertainty
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A Bayesian framework for modeling and testing the temperature-dependent residual compressive strength of concrete
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Optimal-transport flow matching for stochastic model updatinga comparison with transitional MCMC-based Bayesian updating
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Veröffentlicht
21.08.2026
Copyright (c) 2026 Matthias Faes, Michael Beer, Julian Behrend, Marco Broccardo, Guohai Chen, Hanshu Chen, Jieyu Chen, Fang Cong, Henrik Ebel, Peter Eberhard, Andrea Franchini, Zhuojia Fu, Yanzhao Gu, Michael Hanss, Hui Huang, Md Maidul Islam, Jafar Jafari-Asl, Balsa Jovanovic, Tom Könecke, Olga Kosheleva, Vladik Kreinovich, Hao-Ming Li, Pei-Pei Li, Qinghe Li, Ruikun Li, Stefano Marelli, Rafi L. Muhanna, Robert L. Mullen, Chiara Nardin, Lukáš Novák, Shenghao Piao, Mario Rosenfelder, Styfen Schär, Jan Schneider, Wei Shi, Panagiotis Spyridis, Bruno Sudret, Ruben Van Coile, Bo-Yu Wang, Dixiong Yang, Xuan-Yi Zhang, Zi-Xin Zhang, Bo-Qi Zhao, Yan-Gang Zhao, Zhibao Zheng, Baihong Zhong, Zhaowei Zhou, Marcos A. Valdebenito, Chao Dang, Lan-Xin Li, Marius Bittner, Parth Tambat, Marco Behrendt, Zhao Liu, Ping Zhu, Zlatan Dimitrov, Petar O. Hristov, Meng-Ze Lyu, Yang-Yi Liu, Jian-Bing Chen, Qitian Lu, Miroslav Vořechovský, Ekaterina Auer, Lorenz Gillner, Julien Alexandre dit Sandretto, Binod K. Yadav, Ghifari Adam Faza, Hans Hallez, David Moens, Reza Naeimaei, Steffen Schön, Elisabeth Ötsch, Hans Neuner, Zhanhua Liang, Jingwen Song, Michael Desch, Mehdi Modares, Valentin Nikolov, Paulo Lucas Figueiredo, José Torres Farinha, Hugo Raposo, António J. Marques Cardoso, Alice do Carmo Duarte Rodrigues, Paula Gonçalves, Peihan Chen, Xinyu Zhu, Pengfei Wei, Kaiwen Li, Mansoureh Shahabi, Y. F. Cui, Johannes O. Royset, Salvatore Russotto, Mario Di Paola, Antonina Pirrotta, Til Lux, Johannes Sundheim, Tania Feiri, Udo Wiens, Marcus Ricker, F. Niklas Schietzold, Felix Harazin, Yoshi Diepelt, Wolfgang Graf, Michael Kaliske, Rongyao Song, Changcong Zhou, Xuanyi Zhao, Weiping Zhang, Juntao Hu, Yu Chen, Edoardo Patelli, Tingting Sun, Jianbing Chen, Bouwe Verkens, L. Bogaerts, Patrick Van Rymenant, Maximilian Schweizer, Marc Fina, Werner Wagner, Steffen Freitag, George Stefanou, Dimitros Savvas, Panagiotis Gavalas, Luigi Schiano, Razhan S. Ahmed, Oleksandr Al-Shboul, David Ringeloth, Vladislav Gudžulić, Stefanie Schoen, Jelena Bijeljić, Ernst Niederleithinger, Iurie Curoşu, Gerrit E. Neu, Jia-Hui Fu, Yi Luo, De-Cheng Feng, Xiao-Qiu Ai, Luyi Li, Hao Wang, Tairan Wang, Sifeng Bi, Emily Zeller (Kapitelautor/in)
Zitationsvorschlag
REC 2026 - 11th International Workshop on Reliable Engineering Computing: Reliability Computations in a Data and Model-Driven World. (2026). TUDObooks. https://doi.org/10.17877/tudobooks-11
