Recent Submissions 

  1. On the seismo-hydromechanical response of a shear zone during hydraulic stimulation 

    Krietsch, Hannes; Villiger, Linus; Doetsch, Joseph; et al. (2019)
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  2. Sparse polynomial chaos expansions for uncertainty quantification and sensitivity analysis 

    Sudret, Bruno (2019)
    Computational models are used nowadays in virtually all fields of applied sciences and engineering to predict the behaviour of complex natural or man-made systems. These so-called simulators usually feature dozens of parameters and are expensive to run, even when taking full advantage of the available computer power. In this respect, uncertainty quantification techniques used to solve reliability, sensitivity, model calibration/inversion ...
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  3. Surrogate models for uncertainty quantification and design optimization 

    Sudret, Bruno; Mai, Chu V.; Marelli, Stefano; et al. (2019)
    Nowadays computational models are used in virtually all fields of applied sciences and engineering to predict the behaviour of complex natural or man-made systems. Also known as simulators, they allow the engineer to assess the performance of a system in-silico, and then optimize its design or operating. Realistic models (e.g. finite element models) usually feature tens of parameters and are costly to run, even when taking full advantage ...
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  4. Surrogate modelling meets machine learning 

    Sudret, Bruno; Lataniotis, Christos; Lüthen, Nora; et al. (2019)
    Complex computational models are used nowadays in all fields of applied sciences to predict the behaviour of natural, economic and engineering systems. High-fidelity simulators are able to capture more and more realistic features by including multi-scale or multi-physics aspects in their governing equations, which can result in high complexity. Although computer power has attained unprecedented levels, it is still not possible to use brute ...
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  5. Data-based sparse polynomial chaos expansions: applications in dynamics and machine learning 

    Sudret, Bruno (2019)
    The links between uncertainty quantification and classical machine learning (ML) have tightened in the past few years. On the one hand, uncertainty propagation techniques are nowadays mainly based on the use of surrogate models such as polynomial chaos expansions (PCE), Gaussian processes or low-rank tensor representations that are constructed in a non-intrusive manner using a batch of computer experiments and dedicated algorithms. On the ...
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  6. Automated point-of-care processing and interpretation of pulse oximetry for global health applications 

    Karlen, Walter (2019)
    Pulse oximetry is an essential monitoring tool for global health applications, but widely underused. Reasons include, besides the high costs and low robustness of existing devices, the difficulty for health care staff to correctly interpret the obtained measurements. Technology for automated interpretation of blood oxygen saturation could bridge this gap.
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  7. Brief Announcement: Efficient Clustering in Unstructured Radio Networks 

    Kuhn, Fabian; Moscibroda, Thomas; Wattenhofer, Roger (2004)
    Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing, PODC '04
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  8. A new moment-independent measure for reliability-sensitivity analysis 

    Schmid, Florian; Marelli, Stefano; Sudret, Bruno (2019)
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  9. Multimodal – Entwicklung des internationalen Güterverkehrs via Basel 

    Bruckmann, Dirk (2013)
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  10. Study of urban grid configurations 

    Ortigosa, Javier; Gayah, Vikash; Menendez, Monica (2013)
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  11. ViWaS: Viable single wagonload production schemes 

    Bruckmann, Dirk; Fumasoli, Tobias; Galonske, Niklas; et al. (2013)
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  12. Waste in Time - Mineralogical and Environmental Aspects of Waste and Waste Disposal 

    Plötze, Michael (2018)
    Scientific Research Abstracts
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  13. The influence of organic matter on clayey soil plasticity and strength 

    Sollecito, Francesca; Emmenegger, Alain; Plötze, Michael; et al. (2019)
    Euroclay 2019 International Conference on Clay Science and Technology. Book of Abstracts
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  14. A ~phased diploid genome assembly of Italian ryegrass 

    Copetti, Dario; Kuon, Joel‑Elias; Yates, Steven A.; et al. (2019)
    GRC2019 Gatersleben Research Conference - Applied Bioinformatics for Crops, 18-20 March, 2019, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben. Book of Abstracts
    Grasses of the genera Lolium and Festuca are the main feed sources for a sustainable livestock production due to their high palatability and biomass production. Since decades, their importance for the agriculture of temperate regions led to the development of new varieties through traditional breeding programs. However, newer crop improvement methods such as genomic selection could benefit from a high-quality reference genome assembly. ...
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  15. Characterization of a Lolium multiflorum diploid assembly 

    Copetti, Dario; Kuon, Joel‑Elias; Yates, Steven; et al. (2019)
    Grasses of the genera Lolium and Festuca are the main feed sources for a sustainable livestock production due to their high palatability and biomass production. Since decades, their importance for the agriculture of temperate regions led to the development of new varieties through traditional breeding programs. However, newer crop improvement methods such as genomic selection could benefit from a high quality reference genome assembly. ...
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  16. Microstructure Investigation of Submarine Sediments for the Environmental Characterisation of a Contaminated Site 

    Miccoli, Daniela; Cotecchia, Federica; Ludovico, Chiara; et al. (2018)
    Scientific Research Abstracts
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  17. Spectroscopic investigations on organic compounds in desert dust particles 

    Baloh, Philipp; Boose, Yvonne; Plötze, Michael; et al. (2018)
    Geophysical Research Abstracts
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  18. Gaze Pattern Analysis in the Assessment of Digital Ulcers in Patients with Systemic Sclerosis 

    Moser, Thomas; Lohmeyer, Quentin; Meboldt, Mirko; et al. (2019)
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