Zobrazeno 1 - 10
of 100
pro vyhledávání: '"HERRMANN, LUKAS"'
We study the approximation of integrals $\int_D f(\boldsymbol{x}^\top A) \mathrm{d} \mu(\boldsymbol{x})$, where $A$ is a matrix, by quasi-Monte Carlo (QMC) rules $N^{-1} \sum_{k=0}^{N-1} f(\boldsymbol{x}_k^\top A)$. We are interested in cases where t
Externí odkaz:
http://arxiv.org/abs/2305.11645
In cryo-electron microscopy, accurate particle localization and classification are imperative. Recent deep learning solutions, though successful, require extensive training data sets. The protracted generation time of physics-based models, often empl
Externí odkaz:
http://arxiv.org/abs/2304.02011
Stabilization of a class of time-varying parabolic equations with uncertain input data using Receding Horizon Control (RHC) is investigated. The diffusion coefficient and the initial function are prescribed as random fields. We consider both cases, u
Externí odkaz:
http://arxiv.org/abs/2302.00751
Approximation rates are analyzed for deep surrogates of maps between infinite-dimensional function spaces, arising e.g. as data-to-solution maps of linear and nonlinear partial differential equations. Specifically, we study approximation rates for De
Externí odkaz:
http://arxiv.org/abs/2207.04950
Autor:
Grohs, Philipp, Herrmann, Lukas
The approximation of solutions to second order Hamilton--Jacobi--Bellman (HJB) equations by deep neural networks is investigated. It is shown that for HJB equations that arise in the context of the optimal control of certain Markov processes the solu
Externí odkaz:
http://arxiv.org/abs/2103.05744
Centered Gaussian random fields (GRFs) indexed by compacta such as smooth, bounded Euclidean domains or smooth, compact and orientable manifolds are determined by their covariance operators. We consider centered GRFs given as variational solutions to
Externí odkaz:
http://arxiv.org/abs/2103.04424
Autor:
Grohs, Philipp, Herrmann, Lukas
In recent work it has been established that deep neural networks are capable of approximating solutions to a large class of parabolic partial differential equations without incurring the curse of dimension. However, all this work has been restricted
Externí odkaz:
http://arxiv.org/abs/2007.05384
Akademický článek
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Akademický článek
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Autor:
Pomeroy, Eva1,2 (AUTHOR) eva.pomeroy@presencing.org, Herrmann, Lukas3 (AUTHOR)
Publikováno v:
Journal of Applied Behavioral Science. Dec2024, Vol. 60 Issue 4, p677-700. 24p.
