38 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of StuttgartDE22 papers
- Simulation Technologies (United States)US2 papers
- American Institute of Aeronautics and AstronauticsUS1 paper
- AO FoundationCH1 paper
- Chalmers University of TechnologySE1 paper
- Delft University of TechnologyNL1 paper
- École Polytechnique Fédérale de LausanneCH1 paper
- Flatiron Health (United States)US1 paper
- Flatiron Institute1 paper
- Heidelberg UniversityDE1 paper
- Karlsruhe Institute of TechnologyDE1 paper
- Laboratoire de Probabilités, Statistique et ModélisationFR1 paper
22 papers
Elucidating contact electrification mechanism of water
Vasily Artemov, Laura Frank, Roman Doronin +6
The open water surface is known to be charged. Yet, the magnitude of the charge and the physical mechanism of the charging remain unclear, causing heated debates across the scienti…
Non-stationary max-stable models with an application to heavy rainfall data
Carolin Forster, Marco Oesting
In recent years, parametric models for max-stable processes have become a popular choice for modeling spatial extremes because they arise as the asymptotic limit of rescaled maxima…
Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs
Birgit Hillebrecht, Benjamin Unger
Prediction error quantification in machine learning has been left out of most methodological investigations of neural networks, for both purely data-driven and physics-informed app…
A continuum mechanical porous media model for vertebroplasty: Numerical simulations and experimental validation
Zubin Trivedi, Dominic Gehweiler, Jacek K. Wychowaniec +4
The outcome of vertebroplasty is hard to predict due to its dependence on complex factors like bone cement and marrow rheologies. Cement leakage could occur if the procedure is don…
Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy
Paul-Christian Bürkner, Maximilian Scholz, Stefan T. Radev
Probabilistic (Bayesian) modeling has experienced a surge of applications in almost all quantitative sciences and industrial areas. This development is driven by a combination of s…
Intuitive Joint Priors for Bayesian Linear Multilevel Models: The R2D2M2 prior
Javier Enrique Aguilar, Paul-Christian Bürkner
The training of high-dimensional regression models on comparably sparse data is an important yet complicated topic, especially when there are many more model parameters than observ…
A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms
Paul-Christian Bürkner, Ilja Kröker, Sergey Oladyshkin +1
Polynomial chaos expansion (PCE) is a versatile tool widely used in uncertainty quantification and machine learning, but its successful application depends strongly on the accuracy…
Certified machine learning: A posteriori error estimation for physics-informed neural networks
Birgit Hillebrecht, Benjamin Unger
Physics-informed neural networks (PINNs) are one popular approach to incorporate a priori knowledge about physical systems into the learning framework. PINNs are known to be robust…
An equivariant neural operator for developing nonlocal tensorial constitutive models
Jiequn Han, Xu-Hui Zhou, Heng Xiao
Developing robust constitutive models is a fundamental and longstanding problem for accelerating the simulation of complicated physics. Machine learning provides promising tools to…
Multilevel Monte Carlo estimators for elliptic PDEs with Lévy-type diffusion coefficient
Andrea Barth, Robin Merkle
General elliptic equations with spatially discontinuous diffusion coefficients may be used as a simplified model for subsurface flow in heterogeneous or fractured porous media. In…
Diagnosis of model-structural errors with a sliding time-window Bayesian analysis
Han-Fang Hsueh, Anneli Guthke, Thomas Wöhling +1
Deterministic hydrological models with uncertain, but inferred-to-be-time-invariant parameters typically show time-dependent model structural errors. Such errors can occur if a hyd…
Subordinated Gaussian Random Fields in Elliptic Partial Differential Equations
Andrea Barth, Robin Merkle
To model subsurface flow in uncertain heterogeneous\ fractured media an elliptic equation with a discontinuous stochastic diffusion coefficient - also called random field - may be…
Information transfer between turbulent boundary layer and porous media
Wenkang Wang, Xu Chu, Adrián Lozano-Durán +2
The interaction between the flow above and below a permeable wall is a central topic in the study of porous media. While previous investigations have provided compelling evidence o…
Estimation of the Spectral Measure from ConvexCombinations of Regularly Varying RandomVectors
Marco Oesting, Olivier Wintenberger
The extremal dependence structure of a regularly varying random vector Xis fully described by its limiting spectral measure. In this paper, we investigate how torecover characteris…
Bayesian inversion for electromyography using low-rank tensor formats
Anna Rörich, Tim A. Werthmann, Dominik Göddeke +1
The reconstruction of the structure of biological tissue using electromyographic data is a non-invasive imaging method with diverse medical applications. Mathematically, this proce…
An empirical study of Linespots: A novel past-fault algorithm
Maximilian Scholz, Richard Torkar
This paper proposes the novel past-faults fault prediction algorithm Linespots, based on the Bugspots algorithm. We analyze the predictive performance and runtime of Linespots comp…
On the generation of periodic discrete structures with identical two-point correlation
Mauricio Fernández, Felix Fritzen
Strategies for the generation of periodic discrete structures with identical two-point correlation are developed. Starting from a pair of root structures, which are not related by…
Decoding the Rejuvenating Effects of Mechanical Loading on Skeletal Maturation using in Vivo Imaging and Deep Learning
Pouyan Asgharzadeh, Oliver Röhrle, Bettina M. Willie +1
Throughout the process of aging, deterioration of bone macro- and micro-architecture, as well as material decomposition result in a loss of strength and therefore in an increased l…
A p-multigrid method enhanced with an ILUT smoother and its comparison to h-multigrid methods within Isogeometric Analysis
R. Tielen, M. Möller, D. Göddeke +1
Over the years, Isogeometric Analysis has shown to be a successful alternative to the Finite Element Method (FEM). However, solving the resulting linear systems of equations effici…
Weak convergence of Galerkin approximations of stochastic partial differential equations driven by additive Lévy noise
Tobias Stüwe, Andrea Barth
This work considers weak approximations of stochastic partial differential equations (SPDEs) driven by Lévy noise. The SPDEs at hand are parabolic with additive noise processes. A…
Evaluating Morphological Computation in Muscle and DC-motor Driven Models of Human Hopping
Keyan Ghazi-Zahedi, Daniel F. B. Haeufle, Guido Montufar +2
In the context of embodied artificial intelligence, morphological computation refers to processes which are conducted by the body (and environment) that otherwise would have to be…
Emission properties of an oscillating point dipole from a gold Yagi-Uda nanoantenna array
S. V. Lobanov, T. Weiss, D. Dregely +3
We investigate numerically the interaction of an oscillating point dipole with a periodic array of optical Yagi-Uda nanoantennas in the weak coupling limit. A very strong near-fiel…