Arnulf Jentzen
researcher
Facts
11 properties- Instance of
- human
- Date of birth
- November 1983
- Place of birth
- Frankfurt
- Occupation
- mathematician
- Educated at
- Goethe University Frankfurt[5]
- Doctoral advisor
- Peter E. Kloeden[6]
- Doctoral student
- Primož Pušnik, Timo Fabian Welti, Ryan Kurniawan, Sebastian Becker, Diyora Salimova[8][9][10][11][12]
- Maintained by WikiProject
- WikiProject Mathematics[14]
🏷️ Also known as
en Arnulf Jentzen
🔖 Identifiers · 18
- ISNI
- 0000000072735656
- ORCID iD
- 0000-0002-9840-3339
- VIAF cluster ID
- 102570821
- Library of Congress Authorities ID
- n2011056353
- GND ID
- 13971555X
- DBLP author ID
- 35/9470
- DDB person (GND) ID
- 13971555X
- Google Knowledge Graph ID
- /g/11c5s75pzt
- Google Scholar author ID
- fymm-XQAAAAJ
- IdRef ID
- 157447340
- J9U entity ID
- 987007446544605171
- MR Author ID
- 824543
Show all 18 identifiers +6
- Mathematics Genealogy Project ID
- 150154
- NUKAT ID
- n2012042830
- Web of Science ResearcherID
- O-8237-2016
- WorldCat Entities ID
- E39PBJrm9JPhvBpCHfjTKvKfMP
- Yale LUX ID
- person/e47b461a-7369-43c2-8695-3aaa1f35c916
- ZbMATH author ID
- jentzen.arnulf
🔗 Connections
Author of 26
Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning,
Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black--Scholes Partial Differential Equations,
An overview on deep learning-based approximation methods for partial differential equations,
A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions,
A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions,
A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations,
Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation,
Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations
+18 more
← Authored by 26
Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning,
Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black--Scholes Partial Differential Equations,
An overview on deep learning-based approximation methods for partial differential equations,
A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions,
A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions,
A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations,
Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation,
Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations
+18 more
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Summary
Arnulf Jentzen was born in November 1983[1] in Frankfurt[2]. He has worked as a mathematician[3].
FAQs
Where was Arnulf Jentzen born?
Arnulf Jentzen was born in Frankfurt[2].
What did Arnulf Jentzen do for work?
Arnulf Jentzen worked as a mathematician[3].
Where did Arnulf Jentzen go to school?
Arnulf Jentzen was educated at Goethe University Frankfurt[5].