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
E-mail: philippe.vonwurstemberger (at) math.ethz.ch
Links:
[Curriculum Vitae]
[Profile on
ResearchGate]
[Profile on
GoogleScholar]
Last update of this homepage: December 22th, 2020
Education
2019 - |
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PhD student in Mathematics at ETH Zurich
under the supervision of Prof. Patrick
Cheridito
and Prof. Arnulf Jentzen |
2018 - 2019 |
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PhD student in Mathematics at ETH Zurich under the supervision of Prof. Arnulf Jentzen |
2016 - 2018 |
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Master of Science in Mathematics, ETH Zurich, Switzerland |
2012 - 2015 |
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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 high-dimensional partial differential equations.
Commun. Comput. Phys. 28 (2020).
[arXiv].
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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].
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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 Black-Scholes partial differential equations.
[arXiv] (2018), 124 pages.
To appear in the Mem. Amer. Math. Soc.
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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].
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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].
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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.,
High-dimensional approximation spaces of artificial neural networks and applications to partial differential equations.
[arXiv] (2020), 32 pages.
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