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
9 papers · 1 filter
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…