Zobrazeno 1 - 10
of 398
pro vyhledávání: '"Bungartz, Hans Joachim"'
Autor:
Ravi, Kislaya, Fediukov, Vladyslav, Dietrich, Felix, Neckel, Tobias, Buse, Fabian, Bergmann, Michael, Bungartz, Hans-Joachim
One of the main challenges in surrogate modeling is the limited availability of data due to resource constraints associated with computationally expensive simulations. Multi-fidelity methods provide a solution by chaining models in a hierarchy with i
Externí odkaz:
http://arxiv.org/abs/2404.11965
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 compute eigenvalues of a large matrix, which may also be dense depending
Externí odkaz:
http://arxiv.org/abs/2311.06115
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 Carlo (HMC) or No-U-Turn Sampling (NUTS) use the first-order derivati
Externí odkaz:
http://arxiv.org/abs/2310.02703
Autor:
Menhorn, Friedrich, Geraci, Gianluca, Seidl, D. Thomas, Marzouk, Youssef M., Eldred, Michael S., Bungartz, Hans-Joachim
Optimization is a key tool for scientific and engineering applications, however, in the presence of models affected by uncertainty, the optimization formulation needs to be extended to consider statistics of the quantity of interest. Optimization und
Externí odkaz:
http://arxiv.org/abs/2305.03103
Autor:
Farcas, Ionut-Gabriel, Peherstorfer, Benjamin, Neckel, Tobias, Jenko, Frank, Bungartz, Hans-Joachim
Multi-fidelity Monte Carlo methods leverage low-fidelity and surrogate models for variance reduction to make tractable uncertainty quantification even when numerically simulating the physical systems of interest with high-fidelity models is computati
Externí odkaz:
http://arxiv.org/abs/2211.10835
Publikováno v:
soon in Springer LNCS Series, Parallel Processing and Applied Mathematics 14th International Conference, PPAM 2022
Training deep neural networks consumes increasing computational resource shares in many compute centers. Often, a brute force approach to obtain hyperparameter values is employed. Our goal is (1) to enhance this by enabling second-order optimization
Externí odkaz:
http://arxiv.org/abs/2208.02017
Autor:
Scheffler, Matthias, Aeschlimann, Martin, Albrecht, Martin, Bereau, Tristan, Bungartz, Hans-Joachim, Felser, Claudia, Greiner, Mark, Groß, Axel, Koch, Christoph T., Kremer, Kurt, Nagel, Wolfgang E., Scheidgen, Markus, Wöll, Christof, Draxl, Claudia
Publikováno v:
Nature 604, 635 (2022)
The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information techno
Externí odkaz:
http://arxiv.org/abs/2204.13240
The Model Order Reduction (MOR) technique can provide compact numerical models for fast simulation. Different from the intrusive MOR methods, the non-intrusive MOR does not require access to the Full Order Models (FOMs), especially system matrices. S
Externí odkaz:
http://arxiv.org/abs/2204.08523