Sascha Ranftl

Sascha Ranftl
Graz University of Technology | TU Graz · Graz Center of Computational Engineering AND Institute of Theoretical & Computational Physics

Phd
Data | Bayes | Uncertainty Quantification + X

About

20
Publications
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55
Citations
Introduction
Overarching research topics: Bayesian probability theory, uncertainty quantification, statistics, data science, machine learning, computer simulations Particular interest in development of Bayesian statistical learning models for uncertainty quantification of computer simulations, computational/biomedical engineering problems, and all aspects related to that Visit my personal website for more info: https://sites.google.com/view/sascha-ranftl You can also find me on GoogleScholar and LinkedIn

Publications

Publications (20)
Article
Full-text available
The quantification of uncertainties of computer simulations due to input parameter uncertainties is paramount to assess a model’s credibility. For computationally expensive simulations, this is often feasible only via surrogate models that are learned from a small set of simulation samples. The surrogate models are commonly chosen and deemed trustw...
Article
Full-text available
An aortic dissection, a particular aortic pathology, occurs when blood pushes through a tear between the layers of the aorta and forms a so-called false lumen. Aortic dissection has a low incidence compared to other diseases, but a relatively high mortality that increases with disease progression. An early identification and treatment increases pat...
Article
Full-text available
Purpose This paper aims to introduce a non-invasive and convenient method to detect a life-threatening disease called aortic dissection. A Bayesian inference based on enhanced multi-sensors impedance cardiography (ICG) method has been applied to classify signals from healthy and sick patients. Design/methodology/approach A 3D numerical model consi...
Article
Full-text available
Computational hemodynamics has received increasing attention recently. Patient‐specific simulations require questionable model assumptions, e.g. for geometry, boundary conditions, and material parameters. Consequently, the credibility of these simulations is much doubted, and rightly so. Yet, the matter may be addressed by a rigorous uncertainty qu...
Preprint
Full-text available
Inhomogeneities in the aortic wall can lead to localized stress accumulations, possibly initiating dissection. In many cases, a dissection results from pathological changes such as fragmentation or loss of elastic fibers. But it has been shown that even the healthy aortic wall has an inherent heterogeneous microstructure. Some parts of the aorta ar...
Poster
Full-text available
What is the Uncertainty or Trustworthiness of a Surrogate? Here is the answer. E-poster presented at RAMSES 2021 at SISSA Trieste, Italy. https://indico.sissa.it/event/43/ Thanks to the organizers for a top-scientific workshop!
Presentation
Full-text available
Did you ever wonder how the error of your surrogate affects your uncertainty quantification and inference? Or how much you can trust the surrogate-based results? Here is the answer: Video: <https://www.youtube.com/watch?v=EPaAX-eK9mE> DOI: <https://doi.org/10.3217/kn2sx-3qs12> REPO: <https://repository.tugraz.at/records/kn2sx-3qs12>
Article
Full-text available
For physicians, it is often crucial to monitor hemodynamic parameters to provide appropriate treatment for patients. Such hemodynamic parameters can be estimated via electrical bioimpedance (EBI) signal measurements. Time dependent changes of the measured EBI signal occur due to several different phenomena in the human body. Most of the time one is...
Preprint
Full-text available
We introduce Bayesian Probability Theory to investigate uncertainty propagation based on meta-models. We approach the problem from the perspective of data analysis, with a given (however almost-arbitrary) input probability distribution and a given "training" set of computer simulations. While proven mathematically to be the unique consistent probab...
Article
Full-text available
In 2000, Kennedy and O’Hagan proposed a model for uncertainty quantification that combines data of several levels of sophistication, fidelity, quality, or accuracy, e.g., a coarse and a fine mesh in finite-element simulations. They assumed each level to be describable by a Gaussian process, and used low-fidelity simulations to improve inference on...
Conference Paper
Full-text available
Aortic dissection is a cardiovascular disease with a disconcertingly high mortality. When it comes to diagnosis, medical imaging techniques such as Computed Tomography, Magnetic Resonance Tomography or Ultrasound certainly do the job, but also have their shortcomings. Impedance cardiography is a standard method to monitor a patients heart function...
Article
The 0.4 K internal temperature of superfluid helium nanodroplets is believed to guarantee a corresponding ground state population of dopant atoms and molecules inside this cryogenic matrix. We have recorded 6s←5p excitation spectra of indium atoms in helium droplets and found two absorption bands separated by about 2000 cm⁻¹, a value close to the s...
Article
Full-text available
This paper employs Bayesian probability theory for analyzing data generated in femtosecond pump-probe photoelectron-photoion coincidence (PEPICO) experiments. These experiments allow investigating ultrafast dynamical processes in photoexcited molecules. Bayesian probability theory is consistently applied to data analysis problems occurring in these...
Article
Full-text available
The observation of chemical reactions on the time scale of the motion of electrons and nuclei has been made possible by lasers with ever shortened pulse lengths. Superfluid helium represents a special solvent that permits the synthesis of novel classes of molecules that have eluded dynamical studies so far. However, photoexcitation inside this quan...

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Projects

Project (1)
Project
In the Lead project a consortium of scientists from biomechanical-, civil-, electrical-, and mechanical engineering, computer science, mathematics, and physics from TU Graz has set itself the goal of unraveling the cause and the formation of the various stages of an aortic dissection (AD). Advanced computational tools and algorithms will be developed to assist clinicians with the diagnosis, treatment, and management of AD patients. In addition, related topics such as the optimization of implants, the better design for tissue engineering and of coatings and stent platforms for drug delivery will be investigated. https://www.tugraz.at/projekte/aortic-dissection/home/