Philippe von Wurstemberger
Philippe von Wurstemberger
Verified email at sam.math.ethz.ch - Homepage
Title
Cited by
Cited by
Year
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations
P Grohs, F Hornung, A Jentzen, P Von Wurstemberger
arXiv preprint arXiv:1809.02362, 2018
712018
Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equations
M Hutzenthaler, A Jentzen, T Kruse, TA Nguyen, P von Wurstemberger
arXiv preprint arXiv:1807.01212, 2018
362018
Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks
M Hutzenthaler, A Jentzen
Electronic Journal of Probability 25, 2020
152020
Strong error analysis for stochastic gradient descent optimization algorithms
A Jentzen, B Kuckuck, A Neufeld, P von Wurstemberger
arXiv preprint arXiv:1801.09324, 2018
92018
Lower error bounds for the stochastic gradient descent optimization algorithm: Sharp convergence rates for slowly and fast decaying learning rates
A Jentzen, P Von Wurstemberger
Journal of Complexity 57, 101438, 2020
72020
Numerical simulations for full history recursive multilevel Picard approximations for systems of high-dimensional partial differential equations
S Becker, R Braunwarth, M Hutzenthaler, A Jentzen, ...
arXiv preprint arXiv:2005.10206, 2020
62020
Overcoming the course of dimensionality with DNNs: Theoretical approximation results for PDEs
P von Wurstemberger
3rd International Conference on Computational Finance (ICCF2019), 86, 2019
2019
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Articles 1–7