03-09-2025 This page is a bit cumbersome to keep up to date. Please refer to my Google Scholar page instead.
Peer-reviewed journal articles
A connection between probability, physics and neural networks. Sascha Ranftl. Proceedings of MaxEnt 2022. Physical Sciences Forum. 5(1): 11
https://doi.org/10.3390/psf2022005011
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Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate. S. Ranftl, M. Rolf-Pissarczyk, G. Wolkerstorfer, A. Pepe, J. Egger, W. von der Linden, G. Holzapfel. Computer Methods in Applied Mechanics and Engineering. Vol. 401, Part B. 115594. 2022.
https://doi.org/10.1016/j.cma.2022.115594. ISSN 0045-7825
A Bayesian Approach to Blood Rheological Uncertainties in Aortic Hemodynamics. S. Ranftl, T.S. Müller, U. Windberger, G. Brenn, and W. von der Linden. International Journal for Numerical Methods in Biomedical Engineering. 2022. Accepted Author Manuscript e3576. https://doi.org/10.1002/cnm.3576 . Data set available here: https://doi.org/10.5281/zenodo.5237189
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Bayesian Inference of Multi-Sensors Impedance Cardiography for Detection of Aortic Dissection., S.Ranftl & V. Badeli, G.M. Melito, A. Reinbacher-Köstinger, W. von der Linden, K. Ellermann, O. Biro. COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 41 No. 3. pp. 824-839. 2021. https://doi.org/10.1108/COMPEL-03-2021-0072
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Cross Entropy Learning for Aortic Pathology Classification of Artificial Multi-Sensor Impedance Cardiography Signals, T. Spindelböck, S.Ranftl, W. von der Linden. Entropy 2021, 23, 1661. https://doi.org/10.3390/e23121661
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Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection, S. Ranftl, G.M. Melito, V. Badeli, A. Reinbacher-Köstinger, K. Ellermann, W. von der Linden. Entropy 22 (1), p. 58. 2020. https://doi.org/10.3390/e22010058
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Bayesian Source Separation of Electrical Bioimpedance Signals, C.Pichler, S.Ranftl, A. Heller, E.Arrigoni, W. von der Linden.
Biomedical Signal Processing and Control 67, 102541. 2021. https://doi.org/10.1016/j.bspc.2021.102541
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Bayesian Analysis of Femtosecond Pump-Probe Photoelectron-Photoion Coincidence Spectra with Fluctuating Laser Intensities, P.Heim, M.Rumetshofer, S.Ranftl, B. Thaler, W.E. Ernst, M. Koch, and W. von der Linden. Entropy 21 (1), p. 93. 2019. https://doi.org/10.3390/e21010093
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Conservation of Hot Thermal Spin-Orbit Population of 2P Atoms in a Cold Quantum Fluid Environment, B. Thaler, R. Meyer, P. Heim, S. Ranftl, J.V. Pototschnig, A.W. Hauser, M. Koch and W.E. Ernst. The Journal of Physical Chemistry A 123 (18), pp. 3977 - 3984. 2019. https://doi.org/10.1021/acs.jpca.9b02920
PDF: Please visit the publisher's site
Femtosecond Photoexcitation Dynamics inside a Quantum Solvent, B. Thaler, S. Ranftl, P. Heim, S. Cesnik, L. Treiber, R. Meyer, A.W. Hauser, W.E. Ernst and M. Koch. Nature Communications 9 (1), pp. 1-6. 2018. https://doi.org/10.1038/s41467-018-06413-9
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Peer-reviewed conference proceedings
Bayesian Surrogate Analysis and Uncertainty Propagation. Phys. Sci. Forum 2021, 3 (6). S. Ranftl and W. von der Linden, W. https://doi.org/10.3390/psf2021003006
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On the Diagnosis of Aortic Dissection with Impedance Cardiography: A Bayesian Feasibility Study Framework with Multi-Fidelity Simulation Data, S. Ranftl, W. von der Linden, et al. Proceedings 2019, 23 (12):1661 (MaxEnt 2019). https://doi.org/10.3390/proceedings2019033024
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Bayesian Analysis of Femtosecond Pump-Probe Photoelectron-Photoion Coincidence Spectra, P.Heim, M.Rumetshofer, S.Ranftl, B. Thaler, W.E. Ernst, M. Koch, and W. von der Linden. Proceedings of the 38th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2018)
Honoured with the Young Author Best Paper Award
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Editorial appointments and peer-reviewing
Editor: Proceedings of the 40th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt2021). Published as Physical Sciences Forum 3 (1): MaxEnt 2021. www.mdpi.com/2673-9984/3/1
Guest Editor: Entropy 23 (12). Special Issue. www.mdpi.com/journal/entropy/special_issues/MaxEnt2021
Reviewer for: ECML-PKDD 2022 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases), Entropy, Risks, Applied Sciences, Mathematics (all MDPI), Proceedings of the Institution of Mechanical Engineers: Part O, Journal of Risk and Reliability, International Journal for Numerical Methods in Biomedical Engineering, Nuclear Instruments and Methods in Physics Research B
Contributed and invited talks
Bayesian Surrogate Analysis and Uncertainty Propagation. 40th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2021). Virtual Venue.
Invited Talk: Bayesian Multi-Fidelity Uncertainty Quantification with Gaussian Processes and an Application to Computational Fluid Dynamics of the Human Aorta. Conference on Uncertainty Quantification 2020. TU Munich
On the Diagnosis of Aortic Dissection with Impedance Cardiography: A Bayesian Feasibility Study Framework with Multi-Fidelity Simulation Data. 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2019). Max-Planck-Institute for Plasma Physics, Garching/Munich
Bayesian Analysis of Femtosecond Pump-Probe Photoelectron-Photoion Coincidence Spectra. 38th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2018). Young Author Best Paper Award. Alan-Turing-Institute, London, UK
Dissertations
Bayesian Uncertainty Quantification of Numerical Simulations of Aortic Dissection, PhD, 2021. Advisors: Wolfgang von der Linden and Gerhard A. Holzapfel
Ultrafast photoinduced ejection dynamics of indium atoms inside superfluid helium nanodroplets, MSc, 2017. Advisor: Markus Koch
Bayesian blind deconvolution regularisation of two-thermocouple measurements with derivative priors, BSc, 2015. Advisor: Wolfgang von der Linden
Inventions - Patents
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