Philippe von Wurstemberger
ETH Zurich
Address:
Philippe von Wurstemberger
RiskLab Switzerland / Group 3
Department of Mathematics
ETH Zurich
Rämistrasse 101
8092 Zürich
Switzerland
Office: Room HG E 66.2
Phone: +41 44 632 6966
Fax: +41 44 632 1104
Email: philippe.vonwurstemberger (at) math.ethz.ch
Links:
[Curriculum Vitae]
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ResearchGate]
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Last update of this homepage: December 22th, 2020
Education
2019  



PhD student in Mathematics at ETH Zurich
under the supervision of Prof. Patrick
Cheridito
and Prof. Arnulf Jentzen 
2018  2019 



PhD student in Mathematics at ETH Zurich under the supervision of Prof. Arnulf Jentzen 
2016  2018 



Master of Science in Mathematics, ETH Zurich, Switzerland 
2012  2015 



Bachelor of Science in Mathematics, ETH Zurich, Switzerland 
Publications and accepted preprints
 Becker, S., Braunwarth, R., Hutzenthaler, M., Jentzen, A., von Wurstemberger, P.,
Numerical simulations for full history recursive multilevel Picard approximations for systems of highdimensional partial differential equations.
Commun. Comput. Phys. 28 (2020).
[arXiv].

Hutzenthaler, M., Jentzen, A., von Wurstemberger, P.,
Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks.
Electron. J. Probab. 25 (2020).
[arXiv].

Grohs, P., Hornung, F., Jentzen, A., von Wurstemberger, P.,
A proof that artificial neural networks overcome the curse of dimensionality in the numerical
approximation of BlackScholes partial differential equations.
[arXiv] (2018), 124 pages.
To appear in the Mem. Amer. Math. Soc.

Hutzenthaler, M., Jentzen, A., Kruse, T., Nguyen, T. A., von Wurstemberger, P.,
Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial
differential equations.
Proc. R. Soc. A 476 (2020).
[arXiv].

Jentzen, A., von Wurstemberger, P.,
Lower error bounds for the stochastic gradient descent optimization algorithm: Sharp convergence rates for slowly and fast decaying learning rates.
J. Complexity 57 (2020).
[arXiv].

Jentzen, A., Kuckuck, B., Neufeld, A., von Wurstemberger, P.,
Strong error analysis for stochastic gradient descent optimization algorithms.
IMA J. Numer. Anal. (2020).
[arXiv].
Preprints
 Beneventano, P., Cheridito, P., Jentzen, A., von Wurstemberger, P.,
Highdimensional approximation spaces of artificial neural networks and applications to partial differential equations.
[arXiv] (2020), 32 pages.
