TY - JOUR A1 - Linke, S. A1 - Kühn, Julius A1 - Nörthemann, K. A1 - Unger, Wolfgang A1 - Moritz, W. T1 - Sensor high throughput screening using photocurrent measurements in silicon N2 - A new high throughput screening method to characterise alloys used as gate metal of Metal/solid Electrolyte/Insulator/Semiconductor (MEIS) gas-sensors was developed. Samples with continuous gradients in alloy concentration for the system Pd1-x-yNiyCoy were analysed regarding H2 sensitivity. First results showed reduced poisoning effects of H2S for Ni concentrations between 4-11 at-% in Pd. T2 - Eurosensors XXVI CY - Kraków, Poland DA - 09.09.2012 KW - LAPS KW - Ternary alloy KW - MEIS sensor KW - H2 sensor KW - High throughput screening PY - 2012 DO - https://doi.org/10.1016/j.proeng.2012.09.366 SN - 1877-7058 VL - 47 SP - 1195 EP - 1198 PB - Elsevier CY - Amsterdam [u.a.] AN - OPUS4-27926 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Agrawal, A. A1 - Tamsen, E. A1 - Unger, Jörg F. A1 - Koutsourelakis, P-S T1 - From concrete mixture to structural design—a holistic optimization procedure in the presence of uncertainties N2 - We propose a systematic design approach for the precast concrete industry to promote sustainable construction practices. By employing a holistic optimization procedure, we combine the concrete mixture design and structural simulations in a joint, forward workflow that we ultimately seek to invert. In this manner, new mixtures beyond standard ranges can be considered. Any design effort should account for the presence of uncertainties which can be aleatoric or epistemic as when data are used to calibrate physical models or identify models that fill missing links in the workflow. Inverting the causal relations established poses several challenges especially when these involve physicsbased models which more often than not, do not provide derivatives/sensitivities or when design constraints are present. To this end, we advocate Variational Optimization, with proposed extensions and appropriately chosen heuristics to overcome the aforementioned challenges. The proposed approach to treat the design process as a workflow, learn the missing links from data/models, and finally perform global optimization using the workflow is transferable to several other materials, structural, and mechanical problems. In the present work, the efficacy of the method is exemplarily illustrated using the design of a precast concrete beam with the objective to minimize the global warming potential while satisfying a number of constraints associated with its load-bearing capacity after 28 days according to the Eurocode, the demolding time as computed by a complex nonlinear finite element model, and the maximum temperature during the hydration. KW - Black-box optimization under uncertainty KW - Mix design KW - Performance oriented design KW - Precast concrete KW - Probabilistic machine learning KW - Sustainable material design PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-615443 DO - https://doi.org/10.1017/dce.2024.18 VL - 5 IS - e20 SP - 1 EP - 32 PB - Cambridge University Press CY - England AN - OPUS4-61544 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Andrés Arcones, Daniel A1 - Weise, M. A1 - Koutsourelakis, P-S. A1 - Unger, Jörg F. T1 - Bias Identification Approaches for Model Updating of Simulation-based Digital Twins of Bridges N2 - Simulation-based digital twins of bridges have the potential not only to serve as monitoring devices of the current state of the structure but also to generate new knowledge through physical predictions that allow for better-informed decision-making. For an accurate representation of the bridge, the underlying models must be tuned to reproduce the real system. Nevertheless, the necessary assumptions and simplifications in these models irremediably introduce discrepancies between measurements and model response. We will show that quantifying the extent of the uncertainties introduced through the models that lead to such discrepancies provides a better understanding of the real system, enhances the model updating process, and creates more robust and trustworthy digital twins. The inclusion of an explicit bias term will be applied to a representative demonstrator case based on the thermal response of the Nibelungenbrücke of Worms. The findings from this work are englobed in the initiative SPP 100+, whose main aim is the extension of the service life of structures, especially through the implementation of digital twins. T2 - EWSHM 2024 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Digital Twins KW - Model Bias KW - SPP100+ KW - Bridge Monitoring PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-622522 DO - https://doi.org/10.58286/30524 SN - 2941-4989 IS - 12 SP - 1 EP - 10 PB - NDT.net GmbH & Co. KG CY - Mayen, Germany AN - OPUS4-62252 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. A1 - Tamsen, Erik A1 - Agrawal, A. A1 - Koutsourelakis, P.-S. ED - Rogge, Andreas ED - Meng, Birgit T1 - Von Messdaten zum optimierten Bauteil durch Kombination von Material- und Strukturdesign N2 - Die Entwicklung eines optimierten Designs für Bauwerke erfordert die Berücksichtigung sowohl des Materials als auch des Strukturdesigns. Ziel des Beitrages ist die Vorstellung eines Designprinzips basierend auf automatisierten Workflows, das am Beispiel eines Biegebalkendesign zur Reduzierung des Treibhauspotenzials vorgestellt wird. Eine ganzheitliche Optimierung berücksichtigt Material- und Strukturdesign. Es werden physikalische Modelle mit Ansätzen aus dem maschinellen Lernen kombiniert, die mit experimentellen Daten kalibriert bzw. trainiert werden. Eine besondere Bedeutung hat dabei die Berücksichtigung von Unsicherheiten. T2 - 11. Jahrestagung des DAfStb mit 63. Forschungskolloquium der BAM Green Intelligent Building CY - Berlin, Germany DA - 16.10.2024 KW - Materialdesign KW - Strukturdesign PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-613003 SN - 978-3-9818564-7-7 SP - 209 EP - 217 PB - Bundesanstalt für Materialforschung und -prüfung (BAM) CY - Berlin AN - OPUS4-61300 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Coelho Lima, Isabela A1 - Robens-Radermacher, Annika A1 - Titscher, Thomas A1 - Kadoke, Daniel A1 - Koutsourelakis, P.-S. A1 - Unger, Jörg F. T1 - Bayesian inference for random field parameters with a goal-oriented quality control of the PGD forwardmodel's accuracy N2 - Numerical models built as virtual-twins of a real structure (digital-twins) are considered the future ofmonitoring systems. Their setup requires the estimation of unknown parameters, which are not directly measurable. Stochastic model identification is then essential, which can be computationally costly and even unfeasible when it comes to real applications. Efficient surrogate models, such as reduced-order method, can be used to overcome this limitation and provide real time model identification. Since their numerical accuracy influences the identification process, the optimal surrogate not only has to be computationally efficient, but also accurate with respect to the identified parameters. This work aims at automatically controlling the Proper Generalized Decomposition (PGD) surrogate’s numerical accuracy for parameter identification. For this purpose, a sequence of Bayesian model identification problems, in which the surrogate’s accuracy is iteratively increased, is solved with a variational Bayesian inference procedure. The effect of the numerical accuracy on the resulting posteriors probability density functions is analyzed through two metrics, the Bayes Factor (BF) and a criterion based on the Kullback-Leibler (KL) divergence. The approach is demonstrated by a simple test example and by two structural problems. The latter aims to identify spatially distributed damage, modeled with a PGD surrogate extended for log-normal random fields, in two different structures: a truss with synthetic data and a small, reinforced bridge with real measurement data. For all examples, the evolution of the KL-based and BF criteria for increased accuracy is shown and their convergence indicates when model refinement no longer affects the identification results. KW - Variational inference KW - Proper generalized decomposition KW - Goal-oriented KW - Digital twin KW - Random field PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-555755 DO - https://doi.org/10.1007/s00466-022-02214-6 SN - 1432-0924 SP - 1 EP - 22 PB - Springer CY - Berlin AN - OPUS4-55575 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Andrés Arcones, Daniel A1 - Weiser, M. A1 - Koutsourelakis, P.-S. A1 - Unger, Jörg F. T1 - Model bias identification for Bayesian calibration of stochastic digital twins of bridges N2 - Simulation-based digital twins must provide accurate, robust, and reliable digital representations of their physical counterparts. Therefore, quantifying the uncertainty in their predictions plays a key role in making better-informed decisions that impact the actual system. The update of the simulation model based on data must then be carefully implemented. When applied to complex structures such as bridges, discrepancies between the computational model and the real system appear as model bias, which hinders the trustworthiness of the digital twin and increases its uncertainty. Classical Bayesian updating approaches aimed at inferring the model parameters often fail to compensate for such model bias, leading to overconfident and unreliable predictions. In this paper, two alternative model bias identification approaches are evaluated in the context of their applicability to digital twins of bridges. A modularized version of Kennedy and O'Hagan's approach and another one based on Orthogonal Gaussian Processes are compared with the classical Bayesian inference framework in a set of representative benchmarks. Additionally, two novel extensions are proposed for these models: the inclusion of noise-aware kernels and the introduction of additional variables not present in the computational model through the bias term. The integration of these approaches into the digital twin corrects the predictions, quantifies their uncertainty, estimates noise from unknown physical sources of error, and provides further insight into the system by including additional pre-existing information without modifying the computational model. KW - Gaussian process KW - KOH KW - Bayesian updating KW - Digital twins KW - Uncertainty quantification PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-615519 DO - https://doi.org/10.1002/asmb.2897 SN - 1526-4025 N1 - This work was supported by “C07 - Data driven model adaptation for identifying stochastic digital twins of bridges” from the Priority Program (SPP) 2388/1 “Hundred plus” of the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) - Project number 501811638. VL - 417 IS - 3 SP - 1 EP - 26 PB - Wiley CY - Chichester AN - OPUS4-61551 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Andrés Arcones, Daniel A1 - Weiser, M. A1 - Koutsourelakis, F.-S. A1 - Unger, Jörg F. T1 - Evaluation of Model Bias Identification Approaches Based on Bayesian Inference and Applications to Digital Twins N2 - In recent years, the use of simulation-based digital twins for monitoring and assessment of complex mechanical systems has greatly expanded. Their potential to increase the information obtained from limited data makes them an invaluable tool for a broad range of real-world applications. Nonetheless, there usually exists a discrepancy between the predicted response and the measurements of the system once built. One of the main contributors to this difference in addition to miscalibrated model parameters is the model error. Quantifying this socalled model bias (as well as proper values for the model parameters) is critical for the reliable performance of digital twins. Model bias identification is ultimately an inverse problem where information from measurements is used to update the original model. Bayesian formulations can tackle this task. Including the model bias as a parameter to be inferred enables the use of a Bayesian framework to obtain a probability distribution that represents the uncertainty between the measurements and the model. Simultaneously, this procedure can be combined with a classic parameter updating scheme to account for the trainable parameters in the original model. This study evaluates the effectiveness of different model bias identification approaches based on Bayesian inference methods. This includes more classical approaches such as direct parameter estimation using MCMC in a Bayesian setup, as well as more recent proposals such as stat-FEM or orthogonal Gaussian Processes. Their potential use in digital twins, generalization capabilities, and computational cost is extensively analyzed. T2 - 5th ECCOMAS Thematic Conference on Uncertainty Quantificationin Computational Sciences and Engineering CY - Athen, Greece DA - 12.06.2023 KW - Model bias KW - Bayesian Uncertainty Quantification KW - Digital Twins KW - Gaussian Processes KW - Statistical Finite Element Method PY - 2023 UR - https://2023.uncecomp.org/ SP - 1 EP - 15 AN - OPUS4-58227 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Giebler, Rainer A1 - Unger, Wolfgang A1 - Schulz, B. A1 - Reiche, J. A1 - Brehmer, L. A1 - Wühn, M. A1 - Wöll, Ch. A1 - Smith, A.P. A1 - Urquhart, S.G. T1 - Near-Edge X-ray Absorption Fine Structure Spectroscopy on Ordered Films of an Amphiphilic Derivate of 2,5-Diphenyl-1,3,4-Oxadiazole KW - NEXAFS KW - OMBD KW - X-ray Absorption Spectroscopy PY - 1999 SN - 0743-7463 SN - 1520-5827 VL - 15 IS - 4 SP - 1291 EP - 1298 PB - American Chemical Society CY - Washington, DC AN - OPUS4-828 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Andrés Arcones, Daniel A1 - Weiser, M. A1 - Koutsourelakis, P-S. A1 - Unger, Jörg F. T1 - Quantifying the uncertainty of predictive simulations in digital twins through the identification of model bias N2 - This work presents a novel approach to quantifying uncertainty in digital twin simulations by addressing model bias through embedded parameter distributions. Traditional Bayesian methods often underestimate uncertainty due to assumptions of model correctness. We propose a hierarchical Bayesian framework combined with Polynomial Chaos Expansion to better capture and propagate uncertainty. The methodology is validated on an analytical example and a real-world case involving thermal deformation predictions of the Nibelungen Bridge, demonstrating improved predictive accuracy and reliability. T2 - fib Symposium 2025 CY - Antibes, France DA - 16.06.2025 KW - Digital Twins KW - Model Bias KW - Predictive simulations KW - Quantifying the uncertainty PY - 2025 SP - 2867 EP - 2873 PB - The fib, Fédération international du béton CY - Antibes, France AN - OPUS4-63629 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Andrés Arcones, Daniel A1 - Weiser, M. A1 - Koutsourelakis, P-S. A1 - Unger, Jörg F. T1 - Embedded Model Bias Quantification with Measurement Noise for Bayesian Model Calibration N2 - A key factor in ensuring the accuracy of computer simulations that model physical systems is the proper calibration of their parameters based on real-world observations or experimental data. Inevitably, uncertainties arise, and Bayesian methods provide a robust framework for quantifying and propagating these uncertainties to model predictions. Nevertheless, Bayesian methods paired with inexact models usually produce predictions unable to represent the observed datapoints. Additionally, the quantified uncertainties of these overconfident models cannot be propagated to other Quantities of Interest (QoIs) reliably. A promising solution involves embedding a model inadequacy term in the inference parameters, allowing the quantified model form uncertainty to influence non-observed QoIs. This paper introduces a more interpretable framework for embedding the model inadequacy compared to existing methods. To overcome the limitations of current approaches, we adapt the existing likelihood models to properly account for noise in the measurements and propose two new formulations designed to address their shortcomings. Moreover, we evaluate the performance of this inadequacy-embedding approach in the presence of discrepancies between measurements and model predictions, including noise and outliers. Particular attention is given to how the uncertainty associated with the model inadequacy term propagates to the QoIs, enabling a more comprehensive statistical analysis of prediction’s reliability. Finally, the proposed approach is applied to estimate the uncertainty in the predicted heat flux from a transient thermal simulation using temperature observations. KW - Model bias KW - Bayesian inference KW - Noise KW - Model updating KW - Quantity of Interest PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-652720 DO - https://doi.org/10.48550/arXiv.2410.12037 SP - 1 EP - 37 PB - arXiv.org AN - OPUS4-65272 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Starkholm, A. A1 - Al-Sabbagh, Dominik A1 - Sarisozen, S A1 - von Reppert, A A1 - Rössle, M A1 - Ostermann, Markus A1 - Unger, E A1 - Emmerling, Franziska A1 - Kloo, L A1 - Svensson, P A1 - Lang, F A1 - Maslyanchuk, O. T1 - Green Fabrication of Sulfonium-Containing Bismuth Materials for High-Sensitivity X-Ray Detection N2 - Organic–inorganic hybrid materials based on lead and bismuth have recently been proposed as novel X- and gamma-ray detectors for medical imaging, non-destructive testing, and security, due to their high atomic numbers and facile preparation compared to traditional materials like amorphous selenium and Cd(Zn)Te. However, challenges related to device operation, excessively high dark currents, and long-term stability have delayed commercialization. Here, two novel semiconductors incorporating stable sulfonium cations are presented, [(CH3CH2)3S]6Bi8I30 and [(CH3CH2)3S]AgBiI5, synthesized via solvent-free ball milling and fabricated into dense polycrystalline pellets using cold isostatic compression, two techniques that can easily be upscaled, for X-ray detection application. The fabricated detectors exhibit exceptional sensitivities (14 100–15 190 µC Gyair−1 cm−2) and low detection limits (90 nGyair s−1 for [(CH3CH2)3S]6Bi8I30 and 78 nGyair s−1 for [(CH3CH2)3S]AgBiI5), far surpassing current commercial detectors. Notably, they maintain performance after 9 months of ambient storage. The findings highlight [(CH3CH2)3S]6Bi8I30 and [(CH3CH2)3S]AgBiI5 as scalable, cost-effective and highly stable alternatives to traditional semiconductor materials, offering great potential as X-ray detectors in medical and security applications. KW - Mechanochemistry KW - X-ray detectors PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-630306 DO - https://doi.org/10.1002/adma.202418626 SP - 1 EP - 10 PB - Wiley VHC-Verlag AN - OPUS4-63030 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -