92 citations · 124 across the 10 of their papers we have counts for
10 papers
Adaptive Parallel-in-Time Integration with Dynamic Resource Management
Keerthi Gaddameedi, Dominik Huber, Martin Schreiber +4
As computational resources continue to grow, the strong-scaling limitations of spatial parallelism motivate the pursuit of additional concurrency in the temporal dimension, particu…
Multi-fidelity Gaussian process surrogate modeling for regression problems in physics
Kislaya Ravi, Vladyslav Fediukov, Felix Dietrich +4
One of the main challenges in surrogate modeling is the limited availability of data due to resource constraints associated with computationally expensive simulations. Multi-fideli…
Efficient and Scalable Kernel Matrix Approximations using Hierarchical Decomposition
Keerthi Gaddameedi, Severin Reiz, Tobias Neckel +1
With the emergence of Artificial Intelligence, numerical algorithms are moving towards more approximate approaches. For methods such as PCA or diffusion maps, it is necessary to co…
Multi-fidelity No-U-Turn Sampling
Kislaya Ravi, Tobias Neckel, Hans-Joachim Bungartz
Markov Chain Monte Carlo (MCMC) methods often take many iterations to converge for highly correlated or high-dimensional target density functions. Methods such as Hamiltonian Monte…
Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification
Ionut-Gabriel Farcas, Benjamin Peherstorfer, Tobias Neckel +2
Multi-fidelity Monte Carlo methods leverage low-fidelity and surrogate models for variance reduction to make tractable uncertainty quantification even when numerically simulating t…
Neural Nets with a Newton Conjugate Gradient Method on Multiple GPUs
Severin Reiz, Tobias Neckel, Hans-Joachim Bungartz
Training deep neural networks consumes increasing computational resource shares in many compute centers. Often, a brute force approach to obtain hyperparameter values is employed.…
Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis
Julia Konrad, Ionut-Gabriel Farcas, Benjamin Peherstorfer +4
The linear micro-instabilities driving turbulent transport in magnetized fusion plasmas (as well as the respective nonlinear saturation mechanisms) are known to be sensitive with r…
Multilevel adaptive sparse Leja approximations for Bayesian inverse problems
Ionut-Gabriel Farcas, Jonas Latz, Elisabeth Ullmann +2
Deterministic interpolation and quadrature methods are often unsuitable to address Bayesian inverse problems depending on computationally expensive forward mathematical models. Whi…
Sensitivity-driven adaptive sparse stochastic approximations in plasma microinstability analysis
Ionut-Gabriel Farcas, Tobias Görler, Hans-Joachim Bungartz +2
Quantifying uncertainty in predictive simulations for real-world problems is of paramount importance - and far from trivial, mainly due to the large number of stochastic parameters…
Modelling social identification and helping in evacuation simulation
I. von Sivers, A. Templeton, F. Künzner +5
Social scientists have criticised computer models of pedestrian streams for their treatment of psychological crowds as mere aggregations of individuals. Indeed most models for evac…