Author of the publication

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Deep neural network approximations for Monte Carlo algorithms

P. Grohs, A. Jentzen, and D. Salimova. (2019)cite arxiv:1908.10828Comment: 45 pages.

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Arnulf Jentzen

Richard Jentzen

Walter Jentzen

Volker Jentzen

Richard Jentzen

Other publications of authors with the same name

On the existence of minimizers in shallow residual ReLU neural network optimization landscapes.S. Dereich, A. Jentzen, and S. Kassing. CoRR, (2023)An overview on deep learning-based approximation methods for partial differential equations.C. Beck, M. Hutzenthaler, A. Jentzen, and B. Kuckuck. CoRR, (2020)Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations.W. E, J. Han, and A. Jentzen. CoRR, (2017)An Improved Maximum Allowable Transfer Interval for Lp -Stability of Networked Control Systems.A. Jentzen, F. Leber, D. Schneisgen, A. Berger, and S. Siegmund. IEEE Trans. Automat. Contr., 55 (1): 179-184 (2010)Overcoming the curse of dimensionality in the numerical approximation of parabolic partial differential equations with gradient-dependent nonlinearities.M. Hutzenthaler, A. Jentzen, and T. Kruse. CoRR, (2019)An Exponential Wagner-Platen Type Scheme for SPDEs.S. Becker, A. Jentzen, and P. Kloeden. SIAM J. Numer. Anal., 54 (4): 2389-2426 (2016)Lower bounds for artificial neural network approximations: A proof that shallow neural networks fail to overcome the curse of dimensionality.P. Grohs, S. Ibragimov, A. Jentzen, and S. Koppensteiner. J. Complex., (August 2023)Deep neural network approximations for Monte Carlo algorithmsP. Grohs, A. Jentzen, and D. Salimova. (2019)cite arxiv:1908.10828Comment: 45 pages.Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory.A. Jentzen, B. Kuckuck, and P. von Wurstemberger. CoRR, (2023)Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations.A. Jentzen, A. Riekert, and P. von Wurstemberger. CoRR, (2023)

BibSonomy

Disambiguation of "Jentzen, Arnulf"

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Deep neural network approximations for Monte Carlo algorithms

Please choose a person to relate this publication to

Arnulf Jentzen

Richard Jentzen

Walter Jentzen

Volker Jentzen

Richard Jentzen

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Jentzen, Arnulf"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Deep neural network approximations for Monte Carlo algorithms

Please choose a person to relate this publication to

Arnulf Jentzen

Richard Jentzen

Walter Jentzen

Volker Jentzen

Richard Jentzen

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Deep neural network approximations for Monte Carlo algorithms