Sascha Ranftl

Sascha Ranftl

Assistant Professor of Mechanical Engineering (begins August 2026)

Location: West Lafayette

School of Mechanical Engineering
Purdue University
585 Purdue Mall
West Lafayette, IN 47907-2088
sranftl@purdue.edu

Degrees

  • Postdoctoral fellow, Division of Applied Mathematics, Brown University, '25
  • Postdoctoral fellow, Courant Institute, New York University, '24
  • Ph.D., Institute of Theoretical & Computational Physics, TU Graz, Austria, '21
  • Diplom-Ingenieur., Engineering Physics, TU Graz, Austria, '17

Research Interests

  • Scientific machine learning
  • Bayesian probability theory
  • Uncertainty quantification
  • Data analysis
  • Scientific computing
  • Computational physics & engineering
  • Machine learning applications in engineering

Biography

  • Principal Investigator, TU Graz, Center for Computational Engineering & Center for Machine Learning, 2022-2024

  • Visiting Scientist, Sante Ingenierie Biologie, Ecole des Mines de Saint-Etienne, France, '24

  • Founder & Chief Scientist, arterioscope FlexCo, '23

  • Simulation Engineer, AVL Power Train Engineering, '16

    • Patents: Ranftl S, Badeli V. Method and device for determining an aortic state. EP4190227B1

    Awards and Recognitions

    • Best Paper Award for “Deep Polynomial Chaos Expansion” at the Conference on Uncertainty in Artificial Intelligence 2025 Workshop on Tractable Probabilistic Modeling. Awarded to my PhD student J. Exenberger.
    • Invitation to Schloss Dagstuhl Leibniz Center for Informatics, Germany, 2026
    • FWF Erwin Schrodinger Fellow '24
    • Horizon Europe Maria Skłodowska-Curie Seal of Excellence '23, awarded by the European Commission
    • Best Dissertation Prize for Outstanding Societal Relevance 2022, “Forum Technik und Gesellschaft”, Austria
    • Best Paper Award, 38th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2018), Alan-Turing-Institute, London, UK
    • MaxEnt 2022 2nd Best Poster Award. Institut Henri Poincare, Paris, France
    • Guest Editor, Entropy, '21
    • BAYSM New Researcher Travel Award '22
    • ISBA Young Researcher Travel Award, International Society for Bayesian Analysis World Meeting Montreal, '22
    • Central European Association for Computational Mechanics Young Researcher Award for Dissertations: Top 5 nomination, '22
    • SIAM Computational Science & Engineering Early Career Travel Award '25, Fort Worth, TX
    • Conference on Uncertainty in Artificial Intelligence (UAI 2023) - Distinction as Top Reviewer
    • Rudolf-Sallinger Science-to-Business Award 2024: Top 10 Finalist
    • PI and Co-PI on multiple grants (FWF, FFG, aws, UFO, OeAD)
    • Multiple invited presentations, press coverage etc.

    Selected Publications

  • Ranftl S. A connection between probability, physics and neural networks. MaxEnt 2022 Proceedings. https://doi.org/10.3390/psf2022005011

  • Ranftl S. Physics-consistency of infinite neural networks. Neural Information Processing Systems: NeurIPS 2023 Workshop on Machine Learning for Physics.

  • Ranftl S, von der Linden W. Bayesian surrogate analysis and uncertainty propagation. MaxEnt 2021 Proceedings. https://doi.org/10.3390/psf2021003006

  • Exenberger J, Ranftl S, Peharz R. Deep Polynomial Chaos Expansion. Conference on Uncertainty in Artificial Intelligence (UAI) 2025 Workshop on Tractable Probabilistic Modeling. https://arxiv.org/pdf/2507.21273. 2025.

  • Ranftl S, Rolf-Pissarczyk M, Wolkerstorfer G, Pepe A, Egger J, von der Linden W, Holzapfel GA. Stochastic modeling of inhomogeneities in the aortic wall and uncertainty quantification using a Bayesian encoder–decoder surrogate. Computer Methods in Applied Mechanics and Engineering. 2022 Nov 1;401:115594. https://doi.org/10.1016/j.cma.2022.115594

  • Ranftl S, Muller TS, Windberger U, Brenn G, von der Linden W. A Bayesian approach to blood rheological uncertainties in aortic hemodynamics. International Journal for Numerical Methods in Biomedical Engineering. 2023;39(4):e3576. https://doi.org/10.1002/cnm.3576

  • Hoffer JG, Ranftl S, Geiger BC. Robust Bayesian target value optimization. Computers & Industrial Engineering. 2023 Jun 1;180:109279.

  • Bonjak D, Schussnig R, Ranftl S, Holzapfel GA, Fries TP. Geometric uncertainty of patient-specific blood vessels and its impact on aortic hemodynamics: A computational study. Computers in Biology and Medicine. 2025 May 1;190:110017. https://doi.org/10.1016/j.compbiomed.2025.110017

  • Guan S, Zhang X, Ranftl S, Qu T. A neural network-based material cell for elasbottomlasticity and its performance in FE analyses of boundary value problems. International Journal of Plasticity. 2023. 171:103811. https://doi.org/10.1016/j.ijplas.2023.103811

  • Heim P, Rumetshofer M, Ranftl S, Thaler B, Ernst WE, Koch M, von der Linden W. Bayesian Analysis of Femtosecond Pump-Probe Photoelectron-Photoion Coincidence Spectra with Fluctuating Laser Intensities. Entropy. 2019;21(1):93.

  • Thaler B, Ranftl S, Heim P, Cesnik S, Treiber L, Meyer R, Hauser AW, Ernst WE, Koch M. Femtosecond photoexcitation dynamics inside a quantum solvent. Nature communications. 2018 ;9(1):4006.

  • Badeli V, Ranftl S, Melito GM, Reinbacher-Kostinger A, Von Der Linden W, Ellermann K, Biro O. Bayesian inference of multi-sensors impedance cardiography for detection of aortic dissection. COMPEL-The international journal for computation and mathematics in electrical and electronic engineering. 2022 May 10;41(3):824-39.

  • Ranftl S, Melito GM, Badeli V, Reinbacher-Kostinger A, Ellermann K, von der Linden W. Bayesian uncertainty quantification with multi-fidelity data and Gaussian processes for impedance cardiography of aortic dissection. Entropy. 2019 Dec 31;22(1):58.