Showing math.NAShow all
3 papers · 1 filter
math.NA2023
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…
math.NA2023
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…
math.NA2022
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…