Philippe von WurstembergerETH Zurich and CUHK-SZ email: philippe.vonwurstemberger (at) math.ethz.ch and philippevw (at) cuhk.edu.cn Links: [Profile on GoogleScholar] [Profile on ResearchGate] I'm a PhD candidate in Mathematics at ETH Zurich's RiskLab and a visiting researcher at the Chinese University of Hong Kong, Shenzhen. My work lies at the intersection of mathematics and deep learning, with a dual focus: firstly, to deepen the theoretical understanding of deep learning through mathematical theory, and secondly, to innovate new deep learning methods for tackling numerical mathematics problems. ## Short CV
## Awards- ETH Medal (2019) for an outstanding Master's thesis
## Invited talks
## Publications and Accepted Preprints- Becker, S., Jentzen, A., MÃ¼ller, M. S., & von Wurstemberger, P.,
*Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing.*Math. Financ.**00**(2023). [arXiv]. - 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]. -
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 Black-Scholes partial differential equations.*Mem. Amer. Math. Soc.**248**(2023). [arXiv]. -
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- Jentzen, A., Riekert, A., von Wurstemberger, P.,
*Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations.*[arXiv] (2023), 22 pages. - 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.
Last update of this homepage: August 15th, 2023 |
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